diff --git a/inputFiles/compositionalMultiphaseWell/dome_kvalue_base.xml b/inputFiles/compositionalMultiphaseWell/dome_kvalue_base.xml index 0e33618f33b..f87057be69d 100644 --- a/inputFiles/compositionalMultiphaseWell/dome_kvalue_base.xml +++ b/inputFiles/compositionalMultiphaseWell/dome_kvalue_base.xml @@ -2,6 +2,7 @@ + + + + + + + + + + @@ -119,6 +129,7 @@ + + + @@ -167,7 +180,9 @@ solidModelName="NULL.SOLID" porosityModelName="ROCK.POROSITY" permeabilityModelName="ROCK.PERMEABILITY"/> + + + + + + + + + + @@ -229,7 +252,9 @@ + + + + + + + + + + + + @@ -393,6 +428,7 @@ maxEventDt="8640000" beginTime="158652000"/> + + + diff --git a/inputFiles/compositionalMultiphaseWell/dome_properties.xml b/inputFiles/compositionalMultiphaseWell/dome_properties.xml index 4b326d5e44e..c95d45c3993 100644 --- a/inputFiles/compositionalMultiphaseWell/dome_properties.xml +++ b/inputFiles/compositionalMultiphaseWell/dome_properties.xml @@ -1,6 +1,7 @@ + + diff --git a/inputFiles/compositionalMultiphaseWell/dome_soreide_whitson_base.xml b/inputFiles/compositionalMultiphaseWell/dome_soreide_whitson_base.xml index eb98b185e18..7ee2c138df6 100644 --- a/inputFiles/compositionalMultiphaseWell/dome_soreide_whitson_base.xml +++ b/inputFiles/compositionalMultiphaseWell/dome_soreide_whitson_base.xml @@ -2,6 +2,7 @@ + + + + + + + + + + @@ -119,6 +129,7 @@ + + + @@ -167,7 +180,9 @@ solidModelName="NULL.SOLID" porosityModelName="ROCK.POROSITY" permeabilityModelName="ROCK.PERMEABILITY"/> + + + + + + + + + + @@ -233,7 +256,9 @@ + + + + + + + + + + @@ -369,6 +402,7 @@ maxEventDt="8640000" beginTime="158652000"/> + + :end-before: + :dedent: 8 + +The injectors are at the bottom of the left flank, in the sand and below the gas-water contact. The producer is on the right flank, above the contact. With this arrangement the injected fluid has to travel up the structure to reach the gas that remains from the depletion. + +------------------------------------------------------------------------ +Regions and rock properties +------------------------------------------------------------------------ +There are 3 reservoir regions defined by 3 ``CellElementRegion`` tags in the input file. The rock is a compressible porous medium (``PressurePorosity``) with a constant permeability. The values given in the model are default values: the porosity and the permeability of every region are overwritten cell by cell by the ``FieldSpecification`` tags of ``dome_properties.xml``, which scale the reference porosity and the three permeability components of each rock type: + +.. literalinclude:: ../../../../../inputFiles/compositionalMultiphaseWell/dome_soreide_whitson_base.xml + :language: xml + :start-after: + :end-before: + :dedent: 4 + :emphasize-lines: 7,8 + +The sand is about ten times more permeable than the clay, and in both rocks the vertical permeability is ten times smaller than the horizontal one, which makes the layering a strong control of the vertical migration of the gas. + +------------------------------------------------------------------------ +Fluid models +------------------------------------------------------------------------ + +The fluid is a mixture of four components: CH4, CO2, H2S and H2O. It is distributed in two phases, a ``gas`` phase and a ``water`` phase, by a two-phase flash. The brine density and viscosity follow the correlations of Phillips et al., with a salinity of 2.3. The pure-component data (molar mass, critical properties and acentric factor) and the binary interaction coefficients, non-zero only for the pairs that involve water, are common to the two models. + +Two fluid descriptions are provided. They differ only by the way the phase equilibrium is computed, and the rest of the input is identical. + +.. list-table:: The two fluid modeling approaches + :widths: 22 39 39 + :header-rows: 1 + :class: align-top + + * - + - Equation of state + - K-values + * - Input file + - ``dome_soreide_whitson_base.xml`` + - ``dome_kvalue_base.xml`` + * - *GEOS* model + - ``CompositionalTwoPhaseFluidPhillipsBrine`` + - ``CompositionalTwoPhaseKValueFluidPhillipsBrine`` + * - Phase equilibrium + - Stability analysis and flash with Peng-Robinson (gas) and S\ |oslash|\ reide-Whitson (water): the equilibrium depends on the composition + - Flash with K-values :math:`K_i = y_i / x_i` read from tables of pressure and temperature: the equilibrium does not depend on the composition + * - Specific input + - ``pressureCoordinates``, ``temperatureCoordinates`` and the tolerances of the stability and flash algorithms + - ``kValueTables``, one table per component + +**Equation-of-state model** + +The ``equationsOfState`` attribute lists one equation of state per phase, in the order of ``phaseNames``. The gas phase uses the Peng-Robinson (PR) cubic equation of state. The aqueous phase uses the S\ |oslash|\ reide-Whitson variant of PR, which modifies the attractive term for water and uses salinity-dependent interaction parameters, so that the solubility of the gases in the brine and the vaporization of water into the gas are represented. This matters here, since the CO2 dissolves into the brine around the plume. + +.. literalinclude:: ../../../../../inputFiles/compositionalMultiphaseWell/dome_soreide_whitson_base.xml + :language: xml + :start-after: + :end-before: + :dedent: 4 + :emphasize-lines: 4,5,15,16,18,19,20,21 + +The fluid properties are evaluated on a pressure-temperature grid, defined by ``pressureCoordinates`` (13 values, from 1 bar to 600 bar) and ``temperatureCoordinates`` (5 values, from 283.15 K to 354.15 K). The grid must bracket all the states visited during the simulation. Here it covers the reservoir temperature (344.15 K), the surface conditions used to convert the well rates (101325 Pa and 288.71 K) and the whole range of the well pressure limits (1 to 50 MPa). + +**K-value model** + +In the K-value variant, the fluid block is replaced by ``CompositionalTwoPhaseKValueFluidPhillipsBrine``. The component data and the equation-of-state names are kept, but the pressure and temperature grid and the tolerances are replaced by ``kValueTables``, which gives, for each component, the name of a table that returns the equilibrium constant as a function of pressure and temperature: + +.. literalinclude:: ../../../../../inputFiles/compositionalMultiphaseWell/dome_kvalue_base.xml + :language: xml + :start-after: + :end-before: + :dedent: 4 + :emphasize-lines: 4,15,17 + +A K-value flash is simpler than a flash based on an equation of state. Its drawback is that the :math:`K_i` do not depend on the composition. It is a good choice when the compositions stay in a range for which the tables are representative, and the comparison of the two models on the same case is one of the purposes of this example. + +**Relative permeability and capillary pressure** + +The relative permeabilities and the gas-water capillary pressures are tables of the water saturation (or of the gas saturation for the gas relative permeability), with one set per rock type. The clay uses a J-function (``JFunctionCapillaryPressure``), which scales a single dimensionless curve with the permeability and the porosity of each cell and with the surface tension, so that the capillary pressure adapts to the local rock quality. + +.. literalinclude:: ../../../../../inputFiles/compositionalMultiphaseWell/dome_soreide_whitson_base.xml + :language: xml + :start-after: + :end-before: + :dedent: 4 + :emphasize-lines: 2,4,7,9 + +.. literalinclude:: ../../../../../inputFiles/compositionalMultiphaseWell/dome_soreide_whitson_base.xml + :language: xml + :start-after: + :end-before: + :dedent: 4 + :emphasize-lines: 2,4,7,9,10 + +The curves are shown below. They show the end points given in the parameter table. The capillary pressure is of the order of 1 to 2 MPa at the irreducible water saturation, which is large compared with the gravity pressure difference between gas and water over tens of meters. This explains why a thick transition zone develops above the contact in the initial state, and why the gas needs a significant pressure to enter the clay. + +.. plot:: ../src/docs/sphinx/basicExamples/depletedReservoir/plot_dome.py plot_saturation_functions + :align: center + :caption: Relative permeabilities (left) and gas-water capillary pressures (right) of the sand (solid lines) and of the clay (dashed lines), as functions of the water saturation. The capillary pressure of the clay is obtained from its J-function with the surface tension of the model and with the clay porosity and horizontal permeability. + +------------------------------------------------------------------------ +Flow solver +------------------------------------------------------------------------ + +The flow is solved by ``CompositionalMultiphaseFVM``, with a two-point flux approximation (TPFA) discretization, on the sand and clay regions only: + +.. literalinclude:: ../../../../../inputFiles/compositionalMultiphaseWell/dome_soreide_whitson_base.xml + :language: xml + :start-after: + :end-before: + :dedent: 4 + +The ``temperature`` attribute fixes the temperature of the reservoir at 344.15 K: the energy balance is not solved. ``useMass="0"`` selects a molar formulation, so that the compositions and the injection streams are expressed as mole fractions. ``targetPhaseVolFractionChangeInTimeStep`` is relaxed to 1.0, so that the time step is selected essentially from the convergence of the Newton method. + +The reservoir and the wells are solved together, in a fully implicit way, by ``CompositionalMultiphaseReservoir``, which also holds the nonlinear and linear solver parameters: + +.. literalinclude:: ../../../../../inputFiles/compositionalMultiphaseWell/dome_soreide_whitson_base.xml + :language: xml + :start-after: + :end-before: + :dedent: 4 + +------------------------------------------------------------------------ +Initial conditions +------------------------------------------------------------------------ + +.. _CO2InjectionDepletedReservoirInitialState: + +**Initial state** + +The initial state is not uniform: the pressure, the composition and the phase saturations are set by a ``HydrostaticEquilibrium`` tag, which computes the capillary-gravity equilibrium of the whole structure from a datum and from a gas-water contact: + +.. literalinclude:: ../../../../../inputFiles/compositionalMultiphaseWell/dome_soreide_whitson_base.xml + :language: xml + :start-after: + :end-before: + :dedent: 4 + +- **Datum.** The pressure is 27 MPa at the elevation -4453.75 m, which is below the contact. This value is far below the pressure of about 45 MPa that a column of brine in hydrostatic equilibrium would give at this depth, as expected in a depleted field. +- **Contact.** ``phaseContacts`` places the gas-water contact at -4400 m. The vertical pressure of each phase is integrated from the datum with the densities of the fluid, and the saturation in each cell results from the capillary pressure at its elevation above the contact. The initial gas saturation therefore increases progressively from the contact up to the crest, with a transition zone whose thickness is controlled by the capillary pressure curves of each rock type. This is why sand and clay, which have different curves, do not have the same saturation at the same elevation. +- **Composition.** The overall composition is given as a function of elevation by one table per component, linearly interpolated between -5000 m and -4000 m. The lower part of the column is water-rich and the upper part is gas-rich, with a methane fraction that increases with elevation and a small amount of H2S. These tables represent the hydrocarbons that remain after the production. +- **Temperature.** A constant table, equal to the temperature of the flow solver. + +For instance, the tables give at the contact (-4400 m) the overall mole fractions 0.52 for CH4, 0.03 for H2S and 0.45 for H2O, with no CO2. The number of increments in the table of hydrostatic pressure is controlled by ``elevationIncrementInHydrostaticPressureTable``, here 1 m. + +**Boundary conditions** + +No boundary condition is imposed. All the outer boundaries of the domain are closed (no-flow), so that the reservoir is a closed system in which the fluid can only enter or leave through the wells. The pressure changes are therefore driven by the compressibility of the fluids and of the rock. + +.. _CO2InjectionDepletedReservoirWellControls: + +**Well controls** + +The three wells are defined in the ``WellManager``. Each well has a main control (``control``), a table that gives the target rate as a function of time, and a limit on the bottom-hole pressure (BHP) that protects the well and the formation. In all of them, ``useSurfaceConditions="1"`` means that the rates are volumetric rates at 101325 Pa and 288.71 K. + +The water injector injects pure water, the gas injector injects a mixture of 85 % CO2 and 15 % CH4 while the producer is controlled by the volumetric rate of the gas phase, with a lower limit on its BHP. + + +.. list-table:: Well controls + :widths: 15 35 50 + :header-rows: 1 + :class: align-top + + * - Well + - *GEOS* input + - Meaning + * - Water injector + - ``control="totalVolRate"`` with ``InjectionVolumeRateConstraint``, ``injectionStream`` = {0, 0, 0, 1} + - Injection of water at the target rate of the table ``WATER.INJECTOR.RATE``, at the reservoir temperature. The BHP must remain below 50 MPa + * - Gas injector + - ``control="totalVolRate"`` with ``InjectionVolumeRateConstraint``, ``injectionStream`` = {0.15, 0.85, 0, 0} + - Injection of the CO2-CH4 mixture at the target rate of the table ``GAS.INJECTOR.RATE``, at the reservoir temperature. The BHP must remain below 50 MPa + * - Producer + - ``control="phaseVolRate"`` for the ``gas`` phase with ``ProductionPhaseVolumeRateConstraint`` + - Production at the target gas rate of the table ``PRODUCER.RATE``. The BHP must remain above 1 MPa + +The BHP constraints are safeguards. When the reservoir cannot deliver or accept the target rate within the pressure limit of the well, the rate cannot be honoured, and the pressure limit becomes the active constraint. The pressure at the well cells shown in the Results section is a useful indicator of the proximity to these limits. + +**Schedule** + +The target rates are given by three tables of the time (in seconds), which are piecewise constant (``interpolation="lower"``): the value at a given time is the value of the last coordinate that is not greater than this time. The first coordinate of each table, -31557600 s (one year before the start), only defines the value of the rate before the first event, which is zero. + +The coordinates are multiples of one year (31557600 s). Reading the tables in years gives the schedule of the first table of this page: + +- the producer table, ``{-1, 0, 5, 8}`` years with the values ``{0, 15, 15, 0}``, gives a production of 15 m\ :sup:`3`/s between 0 and 8 years. +- the water injector table, ``{-1, 5, 8, 100}`` years with the values ``{0, 0.2, 0, 0}``, gives an injection of 0.2 m\ :sup:`3`/s between 5 and 8 years; +- the gas injector table, ``{-1, 13, 18}`` years with the values ``{0, 20, 0}``, gives an injection of 20 m\ :sup:`3`/s between 13 and 18 years. + +Since the producer remains open during the water flooding, the water injection is accompanied by production, and the period between 8 and 13 years, during which all the wells are closed, separates the water flooding from the gas injection. + +------------------------------------------------------------------------ +Events and output +------------------------------------------------------------------------ + +The simulation covers 20 years. The ``maxTime`` of the events is slightly larger than 20 years (632016000 s), so that the output at 20 years is reached. The events are of three kinds. + +- **Solver applications.** The coupled solver is applied by three periodic events, which differ by the maximum time step (``maxEventDt``). The maximum time step is 100 days (8640000 s) before 5 years and after 5 years and 10 days. Between 5 years and 5 years and 10 days, it is limited to 1 day (86400 s), in order to handle the sudden start of the water injection. In all the cases, the actual time step is chosen by the solver from the convergence of the Newton method, and it is truncated in order to reach the output times. +- **Outputs.** The VTK output is written every half year (15778800 s), and ``targetExactTimestep="1"`` forces the time steps to end exactly at the output times. Restart files are written every 50 cycles. +- **Statistics.** The ``CompositionalMultiphaseStatistics`` task is executed every year (31557600 s). With ``computeRegionStatistics="1"`` and ``writeCSV="1"``, it writes the average pressure and the mass of every phase and component in each region. These files give the mass balance of the injected CO2, which is not available from the maps of the Results section. + +The output and the statistics tasks are defined in the following blocks: + +.. literalinclude:: ../../../../../inputFiles/compositionalMultiphaseWell/dome_soreide_whitson_base.xml + :language: xml + :start-after: + :end-before: + :dedent: 2 + :emphasize-lines: 3,6 + +.. literalinclude:: ../../../../../inputFiles/compositionalMultiphaseWell/dome_soreide_whitson_base.xml + :language: xml + :start-after: + :end-before: + :dedent: 2 + :emphasize-lines: 3,6,8 + +------------------------------------------------------------------------ +Post-processing and reporting +------------------------------------------------------------------------ + +The VTK output of *GEOS* is a ``.pvd`` index file that points to one ``.vtm`` file per output time, each one being split in one ``.vtu`` file per region and per MPI rank. A couple of scripts are included for post-processing this data into and hdf5 format that can then be used to generate the images in this documentation. The scripts are: + +.. code-block:: console + + src/docs/sphinx/basicExamples/depletedReservoir/dome_vtk_to_hdf5.py + src/docs/sphinx/basicExamples/depletedReservoir/plot_dome.py + +``dome_vtk_to_hdf5.py`` converts the whole VTK output into one HDF5 file, independent of the number of MPI ranks. It reads the ``.pvd`` file, merges the ranks of each region, orders the cells in the same way at every time, and stores the relevant datasets. + +.. code-block:: console + + python dome_vtk_to_hdf5.py VTK.OUTPUT.pvd -o dome_results.hdf5 + + +------------------------------------------------------------------------ +Results +------------------------------------------------------------------------ + +**Operations and well response** + +The wells do not follow the target rates of the schedule for most of the simulation. The three figures below show, for each well, the bottom-hole pressure (BHP, black, left axis) and the surface rates actually obtained (colored, right axes). When the reservoir cannot deliver or accept the target rate within the BHP limit of a well, the limit becomes the active constraint and the rate is a result of the simulation, not an input. + +**Producer.** The producer starts at the target gas rate of 15 m\ :sup:`3`/s, with a BHP that falls from about 24 MPa. The BHP reaches the lower limit of 1 MPa after about two years, and stays there until the well is closed at 8 years. From that moment the well is controlled by the pressure limit, and the gas rate decays progressively (about 1 m\ :sup:`3`/s after five years, and close to zero after six years). The gas produced over the eight years is therefore only a fraction (roughly a third) of what the target would give. A small water rate is produced during the first 3.6 years. It is followed by a period without water, and then by a sharp increase of the water rate from about 6 years until the closure, while the gas rate is already negligible. This is consistent with the arrival of the injected water at the producer. + +.. plot:: ../src/docs/sphinx/basicExamples/depletedReservoir/plot_dome.py plot_well_producer + :align: center + :caption: Producer: bottom-hole pressure (black), gas rate (red) and water rate (blue). The gas rate follows the target of 15 m3/s until the BHP reaches its lower limit of 1 MPa, after about two years. + +**Water injector.** The injector is limited by its upper BHP limit of 50 MPa from the first day of injection. The water rate peaks at about 0.085 m\ :sup:`3`/s, which is less than half of the target of 0.2 m\ :sup:`3`/s, and decreases to about 0.04 m\ :sup:`3`/s at 8 years. The injectivity is therefore low, and decreases as the pressure builds up in the closed reservoir. Only a fraction of the target volume of water is injected (well under half). + +.. plot:: ../src/docs/sphinx/basicExamples/depletedReservoir/plot_dome.py plot_well_water_injector + :align: center + :caption: Water injector: bottom-hole pressure (black) and water rate (blue). The BHP is at its upper limit of 50 MPa during the whole injection, and the rate stays below the target of 0.2 m3/s. + +**Gas injector.** The gas injector follows the target of 20 m\ :sup:`3`/s for about half a year only. The BHP rises quickly to the upper limit of 50 MPa, and the well is then controlled by the pressure. The rate falls within one to two years to a value close to zero, and the BHP stays at 50 MPa until the end of the injection at 18 years. Almost all the CO2-CH4 mixture is injected during the first year, and the volume injected is only a small fraction of the target (roughly 10 to 15 %). The last three years of the injection period inject very little. + +.. plot:: ../src/docs/sphinx/basicExamples/depletedReservoir/plot_dome.py plot_well_gas_injector + :align: center + :caption: Gas injector: bottom-hole pressure (black) and gas rate (red). The target of 20 m3/s is held for about half a year, after which the BHP reaches its upper limit of 50 MPa and the rate decays to nearly zero. + +These histories show that the response of the field is controlled by the pressure limits, and that the capacity of the closed reservoir to accept fluid is the limiting factor. The producer exhausts the pressure available for production within two years. The injectors reach their pressure limit almost immediately, because the volume of the reservoir is limited and no fluid can leave it, so every volume injected increases the pressure. The stages of the schedule remain the periods during which the wells are open, but the volumes of fluid produced and injected are smaller than the targets of the input suggest. + +**Pressure** + +The pressure maps give the absolute pressure at the key times. The initial map is a hydrostatic distribution: the pressure increases with depth, with a gradient that changes at the gas-water contact because the gas is lighter than the water. In absolute values, the largest part of the color range is due to this vertical gradient, and the changes caused by the operations are only a fraction of it. + +.. plot:: ../src/docs/sphinx/basicExamples/depletedReservoir/plot_dome.py plot_pressure + :align: center + :caption: Pressure (MPa) at the key times. The pressure increases with depth, and the gradient changes at the gas-water contact (dashed line). + +The second figure removes the hydrostatic distribution and shows the change of pressure with respect to the initial state. It is the most direct picture of the effect of the operations: blue (decrease) is created by the production, and red (increase) is created by the injections. + +.. plot:: ../src/docs/sphinx/basicExamples/depletedReservoir/plot_dome.py plot_pressure_change + :align: center + :caption: Change of pressure with respect to the initial state (MPa) at the key times. Blue is a decrease and red an increase of pressure, on a color scale that is symmetric about zero. + +- **Depletion (0 - 5 years).** The production lowers the pressure first around the producer. The disturbance then extends toward the whole gas-filled part of the structure, and into the water leg. It travels easily in the sand and is slowed down by the clay, which has a lower permeability. +- **Water flooding (5 - 8 years).** The pressure rises around the injector, at the bottom of the left flank. As the producer is still open, a pressure gradient develops between the two wells, and the reservoir is on average repressurized less than the injector region. +- **Shut-in (8 - 13 years).** The pressure field becomes smoother, as the high and low pressure regions equilibrate. The map at 13 years is the starting point of the gas injection. +- **Gas injection (13 - 18 years).** The pressure rises around the injector, and the average pressure of the reservoir increases with the injected volume. The pressure build-up at the well is the quantity to compare with the limit of the BHP. +- **Post-injection (18 - 20 years).** The overpressure of the injector relaxes, while the plume continues to move. + +**Gas saturation** + +The gas saturation shows where the gas is, and its evolution is the key to understanding the other maps. + +.. plot:: ../src/docs/sphinx/basicExamples/depletedReservoir/plot_dome.py plot_gas_saturation + :align: center + :caption: Gas saturation at the key times. The gas is initially located above the gas-water contact (dashed line), with a transition zone that depends on the rock type. + +At the initial time the gas occupies the upper part of the dome, above the contact. The saturation increases from the contact to the crest, with a transition zone governed by the capillary pressure curves. As the sand and the clay have different curves, the saturation changes across their interface. + +- During the depletion and the water flooding the injectors are below the contact, so the water that they inject mainly pushes the contact up and repressurizes the structure. The changes of gas saturation remain limited to the vicinity of the contact. +- During the gas injection the gas is injected below the contact, in the water leg. As it is lighter than the water, it rises, with a plume that is formed at the injector and moves up the structure, under the clay, toward the crest, and spreads in the sand. The plume is fed during the five years of injection and its volume grows continuously. +- After the end of the injection the plume keeps migrating and spreading under the effect of gravity, even if the pressure at the well relaxes. The inactive caprock closes the top of the domain. The clay, which has a lower permeability and a higher capillary pressure, slows down the vertical migration of the gas, and the gas tends to accumulate under it. + +**Composition: CO2 and CH4** + +**Methane.** The initial map shows the native gas: an overall CH4 fraction of about 0.8 in the upper part of the dome, and essentially zero below, in the water leg. The base of the methane-rich zone lies well above the dashed line that marks the elevation given in ``phaseContacts``. + +.. plot:: ../src/docs/sphinx/basicExamples/depletedReservoir/plot_dome.py plot_ch4_fraction + :align: center + :caption: Overall mole fraction of CH4 at the key times. The native methane is produced and displaced during the first eight years, and is almost absent when the gas injection starts. The color scale is the same in all the panels. + +- **Depletion (0 - 5 years).** The overall CH4 fraction in the gas zone falls from about 0.8 to about 0.2. The production removes gas and the water takes its place, so the overall methane fraction decreases even where gas remains. +- **Water flooding (5 - 8 years).** The methane almost completely disappears from the structure. At 8 years, only a small pocket at the very top of the dome, between about x = 1300 m and x = 1800 m, keeps an overall fraction of about 0.7. The producer cell no longer contains methane at this time. This is consistent with the producer history, where the gas rate has decayed to almost zero and the water rate increases. +- **Shut-in and gas injection (8 - 20 years).** On a scale that goes up to 0.9 the maps are dark everywhere. The only visible traces are a few percent of methane at the top of the dome, and a very faint signature in the region of the plume, which corresponds to the 15 % of CH4 in the injected mixture. + +The consequence is important for the injection stage. When the CO2 is injected, there is almost no native gas left to displace or to mix with. The plume is made of the injected mixture alone. + +**Carbon dioxide.** The CO2 maps show a compact plume with a sharp boundary. + +.. plot:: ../src/docs/sphinx/basicExamples/depletedReservoir/plot_dome.py plot_co2_fraction + :align: center + :caption: Mole fraction of CO2 in the gas phase during and after the gas injection. Inside the plume the value is about 0.85, that of the injected mixture, and it is zero elsewhere. The color scale is the same in all the panels. + +Inside the plume, the CO2 fraction of the gas is about 0.85 everywhere, which is the value of the injected mixture, and it is zero outside. The transition at the boundary is only a few cells thick. This agrees with the CH4 maps: the injected gas does not meet native gas on its way, so its composition is not diluted. + +- **0.5 year after the start of the injection (13.5 years).** The plume is a lens around the injectors, about 700 m wide. It extends from close to the base of the domain up to about the initial gas-water contact, above and below the depth of the injector. +- **2.5 years after the start (15.5 years).** The plume has extended to about x = 1050 m. Its top is flat, at about -4290 m, and appears to follow the interface between sand and clay: the gas fills the sand up to the clay and is held below it. The base rises toward the front. +- **End of the injection (18 years).** The front is at about x = 1350 m and has climbed to about -4220 m. The plume has the shape of a wedge that follows the top of the sand up the flank of the dome. +- **End of the simulation (20 years).** The front is at about x = 1550 m and about -4180 m. Between 18 and 20 years the front advances by about 200 m, although the injector is closed at 18 years and had been injecting very little for several years before, as the history of the well shows. This late growth is driven by gravity and by the relaxation of the pressure around the well, not by the injection itself. + +Near the injectors the plume fills the whole thickness of the sand, from the base of the domain to the clay. Within the 20 years of the simulation it stays on the left side of the dome. Its front remains about 500 m from the producer, and it does not reach the crest. + +**Conclusion** + +The example shows how the stages of a depleted-field storage project are driven in *GEOS* by time tables: production, water flooding, shut-in and CO2 injection, on a heterogeneous structure initialized by capillary-gravity equilibrium. The injection of the CO2-CH4 mixture below the contact leads to a plume that rises and spreads under the clay and the caprock, while the pressure of the field is first lowered by the production, then partially restored by the injections. The two fluid descriptions can be compared on the same case by running ``dome_kvalue_bench.xml`` instead of ``dome_soreide_whitson_bench.xml``, converting its output, and building the pictures again. + +**Feedback on this example** + +For any feedback on this example, please submit a `GitHub issue on the project's GitHub page `_. + +------------------------------------------------------------------ +References +------------------------------------------------------------------ + +- Peng, D.-Y., and Robinson, D. B. 1976. A new two-constant equation of state. *Industrial & Engineering Chemistry Fundamentals*, 15(1), 59-64. +- S\ |oslash|\ reide, I., and Whitson, C. H. 1992. Peng-Robinson predictions for hydrocarbons, CO2, N2, and H2S with pure water and NaCl brine. *Fluid Phase Equilibria*, 77, 217-240. +- Phillips, S. L., Igbene, A., Fair, J. A., Ozbek, H., and Tavana, M. 1981. A technical databook for geothermal energy utilization. Lawrence Berkeley Laboratory Report LBL-12810. diff --git a/src/docs/sphinx/basicExamples/depletedReservoir/GAS.INJECTOR.CONTROL.csv b/src/docs/sphinx/basicExamples/depletedReservoir/GAS.INJECTOR.CONTROL.csv new file mode 100644 index 00000000000..0d08349707e --- /dev/null +++ b/src/docs/sphinx/basicExamples/depletedReservoir/GAS.INJECTOR.CONTROL.csv @@ -0,0 +1,790 @@ +Time [s],dt[s],BHP [Pa],Total rate [mol/s],Total surface volumetric rate [sm3/s],Phase0 surface volumetric rate [sm3/s],Phase1 surface volumetric rate [sm3/s],Component0 rate [mol/s],Component1 rate [mol/s],Component2 rate [mol/s],Component3 rate [mol/s] +0,86400,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +86400,172800,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +259200,316941,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +576141,612223,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +1.18836e+06,1.16372e+06,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +2.35209e+06,2.1451e+06,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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a/src/docs/sphinx/basicExamples/depletedReservoir/dome_kvalue_bench.h5 b/src/docs/sphinx/basicExamples/depletedReservoir/dome_kvalue_bench.h5 new file mode 100644 index 00000000000..4eea4a10877 Binary files /dev/null and b/src/docs/sphinx/basicExamples/depletedReservoir/dome_kvalue_bench.h5 differ diff --git a/src/docs/sphinx/basicExamples/depletedReservoir/dome_soreide_whitson_bench.h5 b/src/docs/sphinx/basicExamples/depletedReservoir/dome_soreide_whitson_bench.h5 new file mode 100644 index 00000000000..523afaf7382 Binary files /dev/null and b/src/docs/sphinx/basicExamples/depletedReservoir/dome_soreide_whitson_bench.h5 differ diff --git a/src/docs/sphinx/basicExamples/depletedReservoir/dome_vtk_to_hdf5.py b/src/docs/sphinx/basicExamples/depletedReservoir/dome_vtk_to_hdf5.py new file mode 100644 index 00000000000..42996fdf4b1 --- /dev/null +++ b/src/docs/sphinx/basicExamples/depletedReservoir/dome_vtk_to_hdf5.py @@ -0,0 +1,428 @@ +""" +Convert the GEOS VTK output of the dome example into a single HDF5 file. + +GEOS writes one ``.vtm`` per output time (plus a ``.pvd`` index) and, inside +each ``.vtm``, one ``.vtu`` per MPI rank and per region. This script reads that +tree and stores everything the documentation needs in one compact, rank +independent file: + + /times/seconds (num_times,) output times [s] + /times/years (num_times,) output times [yr] (1 yr = 31557600 s) + /mesh/center (num_cells, 3) cell centres x, y, z [m] + /mesh/region (num_cells,) 0 = SAND, 1 = CLAY, 2 = CAPROCK ... + (attribute ``region_names`` lists the names) + /fields/ (num_times, num_cells) one dataset per scalar field, NaN in the + cells where the field does not exist + (e.g. the inactive CAPROCK) + +Field names +----------- +Scalar arrays keep their VTK name (``pressure``). Multi-component arrays are +split into one dataset per component, named ``_``: + + phaseVolumeFraction_gas, phaseVolumeFraction_water + globalCompFraction_CH4, globalCompFraction_CO2, globalCompFraction_H2S, ... + + The component labels are taken from the VTK file when it carries them. When + it does not, they are rebuilt from ``--phases`` according + to the number of components of the array. + + Usage +----- + python dome_vtk_to_hdf5.py VTK.OUTPUT.pvd -o dome_results.hdf5 + python dome_vtk_to_hdf5.py VTK.OUTPUT.pvd --list-fields + python dome_vtk_to_hdf5.py VTK.OUTPUT.pvd --times-years 0 5 8 13 15.5 18 20 + python dome_vtk_to_hdf5.py VTK.OUTPUT.pvd --fields pressure 'phaseVolumeFraction*' + +Requirements: numpy, h5py, vtk (``pip install numpy h5py vtk``). +""" +import argparse +import datetime +import fnmatch +import os +import sys +import xml.etree.ElementTree as xml_tree +import numpy +import vtk +from vtk.util import numpy_support + +SECONDS_PER_YEAR = 31557600.0 +DEFAULT_FIELDS = ("pressure", "phaseVolumeFraction", "globalCompFraction") +DEFAULT_PHASES = ("gas", "water") + +# -------------------------------------------------------------------------- +# Index files +# -------------------------------------------------------------------------- +def read_pvd(pvd_path): + """ + Parse a ParaView Data (PVD) file to extract simulation time steps. + + Parameters: + pvd_path (str): The file path to the .pvd file. + + Returns: + list: A time-sorted list of tuples, where each tuple contains: + - (float) time_in_seconds + - (str) absolute_vtm_path + """ + root_element = xml_tree.parse(pvd_path).getroot() + base_directory = os.path.dirname(os.path.abspath(pvd_path)) + time_steps = [] + + for dataset in root_element.iter("DataSet"): + time_value = float(dataset.attrib["timestep"]) + file_path = os.path.normpath(os.path.join(base_directory, dataset.attrib["file"])) + time_steps.append((time_value, file_path)) + + time_steps.sort(key=lambda step: step[0]) + return time_steps + + +def region_files(vtm_path): + """ + Map region names to their corresponding list of .vtu files. + + Parameters: + vtm_path (str): The file path to the .vtm file. + + Returns: + dict: A dictionary mapping region names to a list of absolute file paths + for the .vtu files belonging to that region. + """ + root_element = xml_tree.parse(vtm_path).getroot() + base_directory = os.path.dirname(os.path.abspath(vtm_path)) + region_to_files_map = {} + + def walk_blocks(block, is_in_cell_regions, current_path): + for child in block: + if child.tag == "Block": + block_name = child.attrib.get("name", "") + walk_blocks(child, is_in_cell_regions or block_name == "CellElementRegion", + current_path + [block_name]) + elif child.tag == "DataSet" and is_in_cell_regions: + # Expected structure: .../CellElementRegion//rank_i + region_name = current_path[-1] + file_path = os.path.normpath(os.path.join(base_directory, child.attrib["file"])) + region_to_files_map.setdefault(region_name, []).append(file_path) + + top_block = root_element.find("vtkMultiBlockDataSet") + if top_block is None: + raise RuntimeError(f"{vtm_path} is not a valid vtkMultiBlockDataSet file") + walk_blocks(top_block, False, []) + + return region_to_files_map + +def read_vtu(file_path): + """ + Read an Unstructured Grid (.vtu) file using VTK. + + Parameters: + file_path (str): The file path to the .vtu file. + + Returns: + vtkUnstructuredGrid: The loaded VTK unstructured grid data. + """ + vtu_reader = vtk.vtkXMLUnstructuredGridReader() + vtu_reader.SetFileName(file_path) + vtu_reader.Update() + return vtu_reader.GetOutput() + +def cell_centers(unstructured_grid): + """ + Compute and return the cell centers for a given grid. + + Parameters: + unstructured_grid (vtkUnstructuredGrid): The mesh grid to process. + + Returns: + numpy.ndarray: A (num_cells, 3) array of cell center coordinates, compressed to float32. + """ + center_filter = vtk.vtkCellCenters() + center_filter.SetInputData(unstructured_grid) + center_filter.Update() + centers_array = numpy_support.vtk_to_numpy(center_filter.GetOutput().GetPoints().GetData()) + return centers_array.astype(numpy.float32) + +def component_labels(array_base_name, num_components, vtk_data_array, phases): + """ + Generate readable names for the components of a multi-component array. + + Parameters: + array_base_name (str): The base name of the array (e.g., 'pressure'). + num_components (int): The number of components in the array. + vtk_data_array (vtkDataArray): The VTK array holding component metadata. + phases (list): List of expected phase names. + + Returns: + list: A list of strings representing the label for each component. + """ + labels = [] + for component_index in range(num_components): + component_name = vtk_data_array.GetComponentName(component_index) + labels.append(component_name if component_name else None) + + if all(labels): + return [label if label.startswith(array_base_name + "_") else f"{array_base_name}_{label}" for label in labels] + + if num_components == len(phases): + inferred_names = list(phases) + else: + inferred_names = [str(component_index) for component_index in range(num_components)] + + return [f"{array_base_name}_{name}" for name in inferred_names] + + +def read_region(region_files_list, phases, fields_to_keep): + """ + Read and concatenate all the rank files of one specific region. + + Parameters: + region_files_list (list): A list of absolute file paths to .vtu files. + phases (list): List of phase names used for component naming. + fields_to_keep (list): Array patterns specifying which fields to export. + + Returns: + tuple: A tuple containing: + - (numpy.ndarray) centers: The sorted cell centers. + - (dict) fields: Dictionary mapping field names to 1D numpy arrays of sorted field data. + """ + data_chunks = [] + + for file_name in sorted(region_files_list): + unstructured_grid = read_vtu(file_name) + num_cells = unstructured_grid.GetNumberOfCells() + if num_cells == 0: + continue + + valid_cells_mask = numpy.ones(num_cells, dtype=bool) # Used to drop ghost cells + cell_data = unstructured_grid.GetCellData() + + for ghost_name, ownership_check in (("vtkGhostType", lambda attr: attr == 0), + ("ghostRank", lambda attr: attr < 0)): + ghost_array = cell_data.GetArray(ghost_name) + if ghost_array is not None: + valid_cells_mask &= ownership_check(numpy_support.vtk_to_numpy(ghost_array).reshape(num_cells, -1)[:, 0]) + + region_data = { + "centers": cell_centers(unstructured_grid)[valid_cells_mask], + "fields": {} + } + + for data_index in range(cell_data.GetNumberOfArrays()): + vtk_array = cell_data.GetAbstractArray(data_index) + if not isinstance(vtk_array, vtk.vtkDataArray): + continue + + array_base_name = vtk_array.GetName() + selected_pattern = None + for pattern in fields_to_keep: + if pattern in array_base_name: + selected_pattern = pattern + break + if selected_pattern is None: + continue + + numpy_data = numpy_support.vtk_to_numpy(vtk_array).astype(numpy.float64).reshape(num_cells, -1)[valid_cells_mask] + + if numpy_data.shape[1] == 1: + region_data["fields"][selected_pattern] = numpy_data[:, 0] + else: + component_labels_list = component_labels(selected_pattern, numpy_data.shape[1], vtk_array, phases) + for comp_index, label in enumerate(component_labels_list): + region_data["fields"][label] = numpy_data[:, comp_index] + + data_chunks.append(region_data) + + if not data_chunks: + return numpy.empty((0, 3), dtype=numpy.float32), {} + + combined_centers = numpy.concatenate([chunk["centers"] for chunk in data_chunks]) + all_field_names = set().union(*[chunk["fields"].keys() for chunk in data_chunks]) + + combined_fields = { + key: numpy.concatenate([chunk["fields"].get(key, numpy.full(len(chunk["centers"]), numpy.nan)) for chunk in data_chunks]) + for key in all_field_names + } + + # Canonical order: sort by rounded (x, y, z) + rounded_centers = numpy.round(combined_centers, 3) + sort_order = numpy.lexsort((rounded_centers[:, 2], rounded_centers[:, 1], rounded_centers[:, 0])) + + return combined_centers[sort_order], {k: v[sort_order] for k, v in combined_fields.items()} + + +def read_step(vtm_file_path, regions, phases, fields_to_keep): + """ + Read all requested data for a single output time step. + + Parameters: + vtm_file_path (str): The file path to the .vtm file for the time step. + regions (list): List of regions to process. + phases (list): List of expected phase names. + fields_to_keep (list): Array patterns specifying which fields to export. + + Returns: + tuple: A tuple containing concatenated arrays across all regions: + - (numpy.ndarray) centers + - (numpy.ndarray) regions + - (dict) fields mapping to concatenated 1D data arrays + """ + region_to_files_map = region_files(vtm_file_path) + + centers_list, region_ids_list, fields_list = [], [], [] + for region_index, region_name in enumerate(regions): + region_centers, region_fields = read_region( + region_to_files_map.get(region_name, []), phases, fields_to_keep + ) + centers_list.append(region_centers) + region_ids_list.append(numpy.full(len(region_centers), region_index, dtype=numpy.int8)) + fields_list.append(region_fields) + + cell_counts = [len(centers) for centers in centers_list] + unique_field_names = sorted(set().union(*[fields.keys() for fields in fields_list])) + + combined_fields = {} + for field_name in unique_field_names: + combined_fields[field_name] = numpy.concatenate( + [fields.get(field_name, numpy.full(count, numpy.nan)) for fields, count in zip(fields_list, cell_counts)] + ) + + return (numpy.concatenate(centers_list), numpy.concatenate(region_ids_list), combined_fields) + + +# -------------------------------------------------------------------------- +# Main +# -------------------------------------------------------------------------- +def select_times(available_steps, requested_years): + """ + Filter the available simulation steps to match requested output times in years. + + Parameters: + available_steps (list): List of all available (time_in_seconds, file_path) tuples. + requested_years (list): List of specific years to export (can be None or empty). + + Returns: + list: A subset of available_steps that are closest to the requested_years. + """ + if not requested_years: + return available_steps + + times_in_years = numpy.array([step[0] for step in available_steps]) / SECONDS_PER_YEAR + picked_steps = [] + + for target_year in requested_years: + closest_index = int(numpy.argmin(numpy.abs(times_in_years - target_year))) + if abs(times_in_years[closest_index] - target_year) > 0.26: # outputs are typically every 0.5 yr + print(f"warning: no output within 0.25 yr of {target_year} yr " + f"(closest: {times_in_years[closest_index]:.2f} yr)", file=sys.stderr) + if available_steps[closest_index] not in picked_steps: + picked_steps.append(available_steps[closest_index]) + + return sorted(picked_steps, key=lambda step: step[0]) + + +def main(arguments=None): + """ + Main entry point for the script. + + Parameters: + arguments (list, optional): Command line arguments. Defaults to sys.argv. + + Returns: + int: Exit status code (0 for success). + """ + parser = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("pvd", help="GEOS VTK index file (e.g. VTK.OUTPUT.pvd)") + parser.add_argument("-o", "--output", default="dome_results.hdf5", + help="Output HDF5 file path (default: %(default)s)") + parser.add_argument("--fields", nargs="+", default=list(DEFAULT_FIELDS), + help="array-name patterns to export; use '*' for everything " + "(default: %(default)s)") + parser.add_argument("--phases", nargs="+", default=list(DEFAULT_PHASES), + help="Phase names for multi-component labeling (default: %(default)s)") + parser.add_argument("--times-years", nargs="+", type=float, default=None, + help="only export the outputs closest to these times [yr]") + parser.add_argument("--list-fields", action="store_true", + help="print the arrays found in the first output and exit") + parsed_args = parser.parse_args(arguments) + + simulation_steps = select_times(read_pvd(parsed_args.pvd), parsed_args.times_years) + if not simulation_steps: + sys.exit("no time step found in " + parsed_args.pvd) + + fields_to_keep = ["*"] if parsed_args.list_fields else parsed_args.fields + print(f"reading {len(simulation_steps)} time steps from {parsed_args.pvd}") + + # Using meaningful variable names instead of the *0 variables + regions = sorted(region_files(simulation_steps[0][1]).keys()) + initial_centers, initial_regions, initial_fields = read_step( + simulation_steps[0][1], regions, parsed_args.phases, fields_to_keep + ) + + if parsed_args.list_fields: + print(f"{len(initial_centers)} cells; fields in the first output:") + for field_name in sorted(initial_fields): + defined_count = int(numpy.sum(~numpy.isnan(initial_fields[field_name]))) + print(f" {field_name:45s} defined in {defined_count} cells") + return 0 + + import h5py + extracted_field_names = sorted(initial_fields) + if not extracted_field_names: + sys.exit("none of the requested fields was found; try --list-fields") + + num_times = len(simulation_steps) + num_cells = len(initial_centers) + + with h5py.File(parsed_args.output, "w") as hdf5_file: + hdf5_file.attrs["title"] = "GEOS dome example - CO2 injection into a depleted reservoir" + hdf5_file.attrs["created"] = datetime.datetime.now().isoformat(timespec="seconds") + hdf5_file.attrs["phases"] = list(parsed_args.phases) + hdf5_file.attrs["seconds_per_year"] = SECONDS_PER_YEAR + hdf5_file.attrs["num_cells"] = num_cells + + times_array = numpy.array([step[0] for step in simulation_steps]) + time_group = hdf5_file.create_group("times") + time_group.create_dataset("seconds", data=times_array) + time_group.create_dataset("years", data=times_array / SECONDS_PER_YEAR) + + # We apply high compression to mesh datasets. VTI-like meshes will compress down significantly. + mesh_group = hdf5_file.create_group("mesh") + mesh_group.create_dataset("center", data=initial_centers, compression="gzip", compression_opts=9) + mesh_group.create_dataset("region", data=initial_regions, compression="gzip", compression_opts=9) + mesh_group.attrs["region_names"] = list(regions) + + fields_group = hdf5_file.create_group("fields") + field_datasets = { + field_name: fields_group.create_dataset(field_name, shape=(num_times, num_cells), dtype="f4", + chunks=(1, num_cells), compression="gzip", + compression_opts=4, shuffle=True, + fillvalue=numpy.nan) + for field_name in extracted_field_names + } + + for field_name in extracted_field_names: + field_datasets[field_name][0, :] = initial_fields[field_name] + + for time_index in range(1, num_times): + print(f" [{time_index + 1:3d}/{num_times}] t = {simulation_steps[time_index][0] / SECONDS_PER_YEAR:7.2f} yr", + flush=True) + step_centers, _, step_fields = read_step( + simulation_steps[time_index][1], regions, parsed_args.phases, fields_to_keep + ) + + if len(step_centers) != num_cells or not numpy.allclose(step_centers, initial_centers, atol=1e-6): + sys.exit(f"cell layout of {simulation_steps[time_index][1]} differs from the first " + "output; the mesh must be fixed in time") + + for field_name in extracted_field_names: + if field_name in step_fields: + field_datasets[field_name][time_index, :] = step_fields[field_name] + + print(f"wrote {parsed_args.output}: {num_times} times, {num_cells} cells, {len(extracted_field_names)} fields") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/src/docs/sphinx/basicExamples/depletedReservoir/plot_dome.py b/src/docs/sphinx/basicExamples/depletedReservoir/plot_dome.py new file mode 100644 index 00000000000..8cb320905b6 --- /dev/null +++ b/src/docs/sphinx/basicExamples/depletedReservoir/plot_dome.py @@ -0,0 +1,750 @@ +""" +Figures of the "CO2 injection into a depleted reservoir" (dome) example. + +Every ``plot_*`` function below creates one figure from the HDF5 file written +by ``dome_vtk_to_hdf5.py``. They are called by the documentation through the +matplotlib ``plot`` directive of Sphinx, e.g. + + .. plot:: ../inputFiles/.../dome/plot_dome.py plot_pressure + +so that no image is stored in the repository: the pictures (including the +rock-type map of the dome) are regenerated from the simulation output every +time the documentation is built. + +Where things are read from +-------------------------- +* results : ``dome_results.hdf5`` next to this script, or the file given by + the environment variable ``DOME_HDF5``; +* wells : ``dome_mesh_bench.xml`` (``InternalWell`` polylines); +* schedule : ``dome_soreide_whitson_base.xml`` (the three ``*.RATE`` tables) + and the gas-water contact (``phaseContacts``). + +Reading the wells, the schedule and the contact from the input files keeps the +figures consistent with the input if the example is modified. + +Stand-alone use +--------------- + python plot_dome.py # write every figure as a PNG + python plot_dome.py plot_pressure --hdf5 run/dome_results.hdf5 --outdir figs +""" +import argparse +import csv +import os +import re +import sys +import warnings +import xml.etree.ElementTree as ET + +import numpy +import matplotlib.pyplot as plt +from matplotlib.colors import ListedColormap, TwoSlopeNorm +from matplotlib.lines import Line2D +from matplotlib.patches import Patch + +# Set global default font size +plt.rcParams.update({'font.size': 16}) + +# -------------------------------------------------------------------------- +# Directories Configuration +# -------------------------------------------------------------------------- +geos_directory = "." +if "sphinx" in sys.modules: + geos_directory = "../../../../.." +data_directory = os.path.normpath(os.path.abspath(os.path.join(geos_directory, "src/docs/sphinx/basicExamples/depletedReservoir"))) +case_directory = os.path.normpath(os.path.abspath(os.path.join(geos_directory, "inputFiles/compositionalMultiphaseWell"))) + +# -------------------------------------------------------------------------- +# User settings +# -------------------------------------------------------------------------- +HDF5_NAME = "dome_soreide_whitson_bench.h5" +MESH_XML = "dome_mesh_bench.xml" +PROPS_XML = "dome_properties.xml" +BASE_XML = "dome_soreide_whitson_base.xml" + +# (time in years, caption). The captions follow the schedule of the example: +# 0-8 yr production (+ water injection from 5 yr), 8-13 yr shut-in, +# 13-18 yr gas injection, 18-20 yr shut-in. +TIMES_ALL = [ + (0.0, "initial state"), + (5.0, "end of depletion"), + (8.0, "end of water flooding"), + (13.0, "end of shut-in, start of gas injection"), + (15.5, "2.5 yr into gas injection"), + (18.0, "end of gas injection"), + (20.0, "end of simulation"), +] +TIMES_CO2 = [ + (13.5, "0.5 yr into gas injection"), + (15.5, "2.5 yr into gas injection"), + (18.0, "end of gas injection"), + (20.0, "end of simulation"), +] + +ROCK_COLORS = ["#00aaff", "#ff0000", "#00ff00"] # SAND, CLAY, CAPROCK +ROCK_NAMES = ["SAND", "CLAY", "CAPROCK"] +CAPROCK_GRAY = "#d9d9d9" + +# Names of the datasets written by dome_vtk_to_hdf5.py (first match wins) +ALIASES = { + "pressure": ["pressure"], + "gas_saturation": ["phaseVolumeFraction_gas", "phaseVolumeFraction_0"], + "CH4": ["globalCompFraction_CH4"], + "CO2": ["phaseCompFraction_gas,CO2"], +} + + +# -------------------------------------------------------------------------- +# Input-file helpers +# -------------------------------------------------------------------------- +_NUM = re.compile(r"[-+]?(?:\d+\.?\d*|\.\d+)(?:[eE][-+]?\d+)?") + + +def extract_numbers(text): + """ + Extract all floating point numbers from a string. + + Args: + text (str): The string containing numerical values. + + Returns: + numpy.ndarray: An array of the extracted float values. + """ + return numpy.array([float(value) for value in _NUM.findall(text)]) + + +def read_wells(path=None): + """ + Read the perforation mid-point (x, y, z) of every well from the mesh file. + + Args: + path (str, optional): The path to the mesh XML file. Defaults to MESH_XML in case_directory. + + Returns: + dict: A dictionary mapping well names to their (x, y, z) coordinate tuples. + """ + file_path = path if path else os.path.join(case_directory, MESH_XML) + wells_dictionary = {} + for well_element in ET.parse(file_path).getroot().iter("InternalWell"): + points = extract_numbers(well_element.attrib["polylineNodeCoords"]).reshape(-1, 3) + perforation = well_element.find("Perforation") + distance = float(perforation.attrib["distanceFromHead"]) if perforation is not None else 0.0 + vector = points[1] - points[0] + wells_dictionary[well_element.attrib["wellRegionName"]] = tuple(points[0] + vector / numpy.linalg.norm(vector) * distance) + return wells_dictionary + + +def read_gwc(path=None): + """ + Read the initial gas-water contact depth from the base XML file. + + Args: + path (str, optional): The path to the base XML file. Defaults to BASE_XML in case_directory. + + Returns: + float: The gas-water contact depth. + """ + file_path = path if path else os.path.join(case_directory, BASE_XML) + for element in ET.parse(file_path).getroot().iter("HydrostaticEquilibrium"): + return float(extract_numbers(element.attrib["phaseContacts"])[0]) + raise ValueError("Gas-water contact not found in XML") + + +def read_table(name, path=None): + """ + Read the coordinates and values of a TableFunction from the base input file. + + Args: + name (str): The name of the TableFunction to extract. + path (str, optional): The path to the base XML file. Defaults to BASE_XML in case_directory. + + Returns: + tuple: A tuple containing (coordinates_array, values_array). + """ + file_path = path if path else os.path.join(case_directory, BASE_XML) + for table_element in ET.parse(file_path).getroot().iter("TableFunction"): + if table_element.attrib.get("name") == name: + return extract_numbers(table_element.attrib["coordinates"]), extract_numbers(table_element.attrib["values"]) + raise KeyError(name) + + +def get_rate_at_time(coordinates, values, time_point): + """ + Get the interpolated value from a table with piecewise constant interpolation. + + Args: + coordinates (numpy.ndarray): The x-coordinates (times) of the table. + values (numpy.ndarray): The y-values (rates) of the table. + time_point (float): The specific time to interpolate the value for. + + Returns: + float: The interpolated rate. + """ + index = numpy.searchsorted(coordinates, time_point, side="right") - 1 + return 0.0 if index < 0 else values[index] + + +def read_schedule(path=None): + """ + Read the rate schedules for the producer, water injector, and gas injector. + + Args: + path (str, optional): The path to the base XML file. Defaults to BASE_XML in case_directory. + + Returns: + dict: A dictionary mapping 'P', 'W', 'G' to their respective (coordinates, values) tables. + Returns None if the files or tables cannot be found. + """ + try: + return {"P": read_table("PRODUCER.RATE", path), + "W": read_table("WATER.INJECTOR.RATE", path), + "G": read_table("GAS.INJECTOR.RATE", path)} + except (KeyError, OSError): + return None + + +PHASE_LABELS = {frozenset(): "shut-in", frozenset("P"): "depletion", + frozenset("PW"): "water flooding", frozenset("G"): "gas injection"} +PHASE_COLORS = {"shut-in": "#eeeeee", "depletion": "#fddbc7", + "water flooding": "#d1e5f0", "gas injection": "#d9f0d3"} + + +def operating_phases(schedule, time_end): + """ + Determine the operating phases over time based on active rate tables. + + Args: + schedule (dict): The dictionary of well rate tables. + time_end (float): The final time point to consider in seconds. + + Returns: + list: A list of tuples (time_start, time_end, phase_label). + """ + time_cuts = {0.0, time_end} + for coordinates, _ in schedule.values(): + time_cuts.update(coord for coord in coordinates if 0.0 < coord < time_end) + sorted_cuts = sorted(time_cuts) + phases = [] + for time_start, time_end_segment in zip(sorted_cuts[:-1], sorted_cuts[1:]): + time_midpoint = 0.5 * (time_start + time_end_segment) + active_wells = frozenset(key for key, (coords, vals) in schedule.items() if get_rate_at_time(coords, vals, time_midpoint) > 0) + phases.append((time_start, time_end_segment, PHASE_LABELS.get(active_wells, "+".join(sorted(active_wells))))) + return phases + + +# -------------------------------------------------------------------------- +# Results access +# -------------------------------------------------------------------------- +def get_hdf5_path(): + """ + Determine the location of the HDF5 results file. + + Returns: + str or None: The path to the HDF5 file if found, otherwise None. + """ + candidates = [os.environ.get("DOME_HDF5"), os.path.join(data_directory, HDF5_NAME), os.path.join(os.getcwd(), HDF5_NAME)] + for candidate in candidates: + if candidate and os.path.isfile(candidate): + return candidate + return None + + +class Results: + """Thin reader that exposes the quasi-2D (x, z) section of the dome.""" + + def __init__(self, path, target_y_coordinate=None): + """ + Initialize the Results reader from an HDF5 file. + + Args: + path (str): Path to the HDF5 file. + target_y_coordinate (float, optional): The target y-coordinate to slice. Defaults to the median y. + """ + import h5py + try: + self.h5 = h5py.File(path, "r") + except Exception as e: + raise RuntimeError(f"Failed to open HDF5 file at {path} {e}") from e + + # Handle updated time keys and conversion + self.time_years = self.h5["times"]["years"][()] + + mesh_group = self.h5["mesh"] + self.center = mesh_group["center"][:] + + # Handle potential missing 'size' and new 'region' key + self.size = mesh_group["size"][:] if "size" in mesh_group else None + self.region = mesh_group["region_id"][:] if "region_id" in mesh_group else mesh_group["region"][:] + + y_slices = numpy.unique(self.center[:, 1].round(3)) + if target_y_coordinate is None: + target_y_coordinate = y_slices[len(y_slices) // 2] + self.selected_y = y_slices[numpy.argmin(numpy.abs(y_slices - target_y_coordinate))] + self.mask = numpy.abs(self.center[:, 1] - self.selected_y) < 1e-3 + center_x = self.center[self.mask, 0].round(3) + center_z = self.center[self.mask, 2].round(3) + self.unique_x, self.unique_z = numpy.unique(center_x), numpy.unique(center_z) + self._index_x = numpy.searchsorted(self.unique_x, center_x) + self._index_z = numpy.searchsorted(self.unique_z, center_z) + self.edges_x, self.edges_z = calculate_edges(self.unique_x), calculate_edges(self.unique_z) + + def get_time_index(self, target_time_years): + """ + Get the closest time index in the results for a target time. + + Args: + target_time_years (float): The target time in years. + + Returns: + int: The index of the closest output time step. + """ + index = int(numpy.argmin(numpy.abs(self.time_years - target_time_years))) + if abs(self.time_years[index] - target_time_years) > 0.3: + warnings.warn(f"no output near {target_time_years} yr; using {self.time_years[index]:.2f} yr") + return index + + def get_dataset(self, field_key): + """ + Retrieve a dataset by key, accounting for aliases. + + Args: + field_key (str): The logical name of the field. + + Returns: + h5py.Dataset: The HDF5 dataset for the field. + """ + for name in ALIASES.get(field_key, [field_key]): + path = name.split(",") + data_set = self.h5["fields"][path[0]] + for p in path[1:]: + data_set = data_set[p] + if data_set is not None: + return data_set + raise KeyError(f"none of {ALIASES.get(field_key, [field_key])} found in the HDF5 file; " + "run dome_vtk_to_hdf5.py --list-fields on the VTK output") + + def map_to_grid(self, values_1d): + """ + Map a 1D array of values to the 2D grid slice. + + Args: + values_1d (numpy.ndarray): 1D array of values corresponding to the active cells. + + Returns: + numpy.ndarray: A 2D array of the values on the grid. + """ + grid = numpy.full((len(self.unique_z), len(self.unique_x)), numpy.nan) + grid[self._index_z, self._index_x] = values_1d[self.mask] + return grid + + def get_field_2d(self, field_key, time_index): + """ + Get a 2D slice of a specific field at a specific time index. + + Args: + field_key (str): The field to retrieve. + time_index (int): The time step index. + + Returns: + numpy.ndarray: 2D array of the requested field. + """ + return self.map_to_grid(self.get_dataset(field_key)[time_index, :].astype(float)) + + def get_region_2d(self): + """ + Get a 2D slice of the region IDs. + + Returns: + numpy.ndarray: 2D array of the region IDs. + """ + return self.map_to_grid(self.region.astype(float)) + + +def calculate_edges(centers): + """ + Calculate grid edges from cell centers. + """ + if len(centers) == 1: + return numpy.array([centers[0] - 0.5, centers[0] + 0.5]) + edges = numpy.empty(len(centers) + 1) + edges[1:-1] = 0.5 * (centers[1:] + centers[:-1]) + edges[0] = centers[0] - 0.5 * (centers[1] - centers[0]) + edges[-1] = centers[-1] + 0.5 * (centers[-1] - centers[-2]) + return edges + + +def load_results(): + """ + Load the Results object from the configured HDF5 file path. + + Returns: + Results or None: The Results object, or None if the file could not be found. + """ + file_path = get_hdf5_path() + if file_path is None: + warnings.warn(f"{HDF5_NAME} not found: run dome_vtk_to_hdf5.py on the GEOS " + "output or set DOME_HDF5") + return None + return Results(file_path, target_y_coordinate=list(read_wells().values())[0][1]) + +def create_placeholder_figure(text="Results file not found"): + """ + Create a placeholder matplotlib figure when data is missing. + """ + figure, axis = plt.subplots(figsize=(8, 1.5)) + axis.text(0.5, 0.5, f"{text}\n(run dome_vtk_to_hdf5.py on the GEOS output " + f"to create {HDF5_NAME})", ha="center", va="center") + axis.axis("off") + return figure + +# -------------------------------------------------------------------------- +# Drawing helpers +# -------------------------------------------------------------------------- +def decorate_axes(axis, results_data, wells, gas_water_contact, add_labels=False): + """ + Draw interfaces, gas-water contact, and wells on one matplotlib axis. + """ + label_color = "white" + + axis.set_facecolor(CAPROCK_GRAY) # inactive cells (CAPROCK) + axis.contour(results_data.unique_x, results_data.unique_z, results_data.get_region_2d(), levels=[0.5, 1.5], colors="k", + linewidths=0.4, alpha=0.5) + axis.axhline(gas_water_contact, color=label_color, ls="--", lw=0.7) + gas_injector = wells.get("GAS.INJECTOR", wells.get("WATER.INJECTOR")) + producer_well = wells.get("PRODUCER") + + for well_node, marker_style, label_name in ((gas_injector, "v", "injectors"), (producer_well, "^", "producer")): + if well_node is not None: + axis.plot(well_node[0], well_node[2], marker=marker_style, ms=12, mfc="k", mec="w", mew=0.8, + zorder=5, clip_on=False) + if add_labels: + axis.annotate("gas-water contact", (results_data.edges_x[-1], gas_water_contact), xytext=(-4, 2), + textcoords="offset points", ha="right", va="bottom", color=label_color) + if gas_injector is not None: + axis.annotate("injectors", (gas_injector[0], gas_injector[2]), xytext=(6, 4), + textcoords="offset points", color=label_color) + if producer_well is not None: + axis.annotate("producer", (producer_well[0], producer_well[2]), xytext=(-6, 4), + textcoords="offset points", ha="right", color=label_color) + axis.set_xlim(results_data.edges_x[0], results_data.edges_x[-1]) + axis.set_ylim(results_data.edges_z[0], results_data.edges_z[-1]) + axis.set_yticks([-4680, -4400, -4000]) + +def get_fraction_limits(arrays): + """ + Calculate appropriate visual limits for fraction data. + """ + max_value = numpy.nanmax([numpy.nanmax(array_data) for array_data in arrays]) + max_value = 1.0 if not numpy.isfinite(max_value) or max_value <= 0 else min(1.0, numpy.ceil(max_value * 20) / 20) + return 0.0, max_value + +def plot_stacked_fields(field_key, target_times, colormap_name, colorbar_label, use_diverging_norm=False, show_as_change=False, + is_fraction=False): + """ + Create one figure with key times stacked vertically sharing one color scale. + """ + results_data = load_results() + if results_data is None: + return create_placeholder_figure() + wells, gas_water_contact = read_wells(), read_gwc() + time_indices = [results_data.get_time_index(time_val) for time_val, _ in target_times] + arrays_to_plot = [results_data.get_field_2d(field_key, index) for index in time_indices] + + if field_key == "pressure": + arrays_to_plot = [array_data / 1e6 for array_data in arrays_to_plot] # Pa -> MPa + if show_as_change: + reference_array = results_data.get_field_2d(field_key, results_data.get_time_index(0.0)) / 1e6 + arrays_to_plot = [array_data - reference_array for array_data in arrays_to_plot] + + if use_diverging_norm: + max_abs_value = numpy.nanmax([numpy.nanmax(numpy.abs(array_data)) for array_data in arrays_to_plot]) or 1.0 + plot_arguments = dict(norm=TwoSlopeNorm(0.0, -max_abs_value, max_abs_value)) + elif is_fraction: + vmin, vmax = get_fraction_limits(arrays_to_plot) + plot_arguments = dict(vmin=vmin, vmax=vmax) + else: + plot_arguments = dict(vmin=numpy.nanmin([numpy.nanmin(array_data) for array_data in arrays_to_plot]), + vmax=numpy.nanmax([numpy.nanmax(array_data) for array_data in arrays_to_plot])) + + num_subplots = len(target_times) + figure, axes_list = plt.subplots(num_subplots, 1, figsize=(10, 1.8 * num_subplots + 0.8), sharex=True, + layout="constrained") + axes_list = numpy.atleast_1d(axes_list) + for subplot_index, (axis, (time_val, caption), array_data, time_index) in enumerate(zip(axes_list, target_times, arrays_to_plot, time_indices)): + color_mesh = axis.pcolormesh(results_data.edges_x, results_data.edges_z, array_data, cmap=colormap_name, shading="flat", + rasterized=True, **plot_arguments) + decorate_axes(axis, results_data, wells, gas_water_contact, add_labels=(subplot_index == 0)) + axis.set_title(f"t = {results_data.time_years[time_index]:g} yr: {caption}", loc="left", pad=2) + axes_list[-1].set_xlabel("x (m)") + figure.supylabel("elevation z (m)") + figure.colorbar(color_mesh, ax=list(axes_list), label=colorbar_label, pad=0.01, shrink=0.8, aspect=30) + plt.show() + return figure + +# -------------------------------------------------------------------------- +# Public figures (one per ``.. plot::`` directive) +# -------------------------------------------------------------------------- +def plot_rock_types(): + """ + Rock-type map of the dome, rebuilt from the cell regions of the output. + """ + results_data = load_results() + if results_data is None: + return create_placeholder_figure() + wells, gas_water_contact = read_wells(), read_gwc() + figure, axis = plt.subplots(figsize=(10, 4.2), layout="constrained") + axis.pcolormesh(results_data.edges_x, results_data.edges_z, results_data.get_region_2d(), cmap=ListedColormap(ROCK_COLORS), + vmin=-0.5, vmax=2.5, shading="flat", rasterized=True) + decorate_axes(axis, results_data, wells, gas_water_contact, add_labels=True) + axis.set_xlabel("x (m)") + axis.set_ylabel("elevation z (m)") + axis.legend(handles=[Patch(color=color_code, label=name) for color_code, name in zip(ROCK_COLORS, ROCK_NAMES)] + + [Line2D([], [], color="k", ls="--", lw=0.7, label="gas-water contact")], + loc="upper center", bbox_to_anchor=(0.5, -0.22), ncol=4, frameon=False) + plt.show() + return figure + + +def plot_pressure(): + """ + Absolute pressure (MPa) at the key times. + + Returns: + matplotlib.figure.Figure: The generated figure. + """ + return plot_stacked_fields("pressure", TIMES_ALL, "jet", "pressure (MPa)") + + +def plot_pressure_change(): + """ + Pressure change with respect to the initial state (MPa). + + Returns: + matplotlib.figure.Figure: The generated figure. + """ + return plot_stacked_fields("pressure", TIMES_ALL[1:], "jet", + "pressure change from t = 0 (MPa)", use_diverging_norm=True, show_as_change=True) + + +def plot_gas_saturation(): + """ + Gas saturation at the key times. + + Returns: + matplotlib.figure.Figure: The generated figure. + """ + return plot_stacked_fields("gas_saturation", TIMES_ALL, "jet", "gas saturation (-)", + is_fraction=True) + + +def plot_co2_fraction(): + """ + CO2 global mole fraction during and after the gas injection. + + Returns: + matplotlib.figure.Figure: The generated figure. + """ + return plot_stacked_fields("CO2", TIMES_CO2, "jet", "CO$_2$ gas mole fraction (-)", + is_fraction=True) + + +def plot_ch4_fraction(): + """ + CH4 global mole fraction at the key times. + + Returns: + matplotlib.figure.Figure: The generated figure. + """ + return plot_stacked_fields("CH4", TIMES_ALL, "jet", "CH$_4$ global mole fraction (-)", + is_fraction=True) + +def _read_well_csv(filename): + """ + Helper to read well rates and BHP from a GEOS control CSV. + Converts time to years and pressure to MPa. + """ + path = os.path.join(data_directory, filename) + if not os.path.isfile(path): + return None + + times, bhps, gas_rates, water_rates = [], [], [], [] + with open(path, 'r') as f: + reader = csv.DictReader(f) + headers = reader.fieldnames + if not headers: + return None + + # Make lookup case-insensitive and robust against trailing spaces + lower_headers = {h: h.strip().lower() for h in headers} + + # Identify columns + t_key = next((h for h in headers if 'time' in lower_headers[h]), None) + bhp_key = next((h for h in headers if 'bottom hole pressure' in lower_headers[h] or 'bhp' in lower_headers[h]), None) + gas_key = next((h for h in headers if 'phase0 surface volumetric' in lower_headers[h] and 'rate' in lower_headers[h]), None) + water_key = next((h for h in headers if 'phase1 surface volumetric' in lower_headers[h] and 'rate' in lower_headers[h]), None) + + for row in reader: + try: + times.append(float(row[t_key]) if t_key and row[t_key] else 0.0) + bhps.append(float(row[bhp_key]) if bhp_key and row[bhp_key] else 0.0) + gas_rates.append(numpy.abs(float(row[gas_key])) if gas_key and row[gas_key] else 0.0) + water_rates.append(numpy.abs(float(row[water_key])) if water_key and row[water_key] else 0.0) + except ValueError: + pass # Skip rows with problematic data + + return { + 'time': numpy.array(times) / 31557600.0, # seconds to years + 'bhp': numpy.array(bhps) / 1e6, # Pa to MPa + 'gas_rate': numpy.array(gas_rates), + 'water_rate': numpy.array(water_rates) + } + +def plot_well(well_name): + """ + Well surface volumetric rates read from CSV files. + Plots producer and gas injector (gas rates) on left axis. + Plots water injector (water rate) on right axis. + """ + fig, ax_p = plt.subplots(figsize=(10, 5), layout="constrained") + + data_well = _read_well_csv(f"{well_name}.csv") + if not data_well: + return create_placeholder_figure("Well control CSV files not found") + + is_producer = "PRODUCER" in well_name.upper() + is_gas = "GAS" in well_name.upper() + ax_w, ax_g = None, None + if is_producer or not is_gas: + ax_w = ax_p.twinx() + if is_producer or is_gas: + ax_g = ax_p.twinx() + + # Plotting gas rates on the left axis + ax_p.plot(data_well['time'], data_well['bhp'], color='k', lw=2, label="BHP") + if ax_g: + ax_g.plot(data_well['time'], data_well['gas_rate'], color='r', lw=2, label="Gas Rate") + if ax_w: + ax_w.plot(data_well['time'], data_well['water_rate'], color='b', lw=2, label="Water Rate") + + ax_p.set_xlabel("time (years)") + ax_p.set_ylabel("BHP (MPa)") + + if ax_w: + ax_w.set_ylabel("water rate (m$^3$/s)", color='b') + ax_w.tick_params(axis='y', labelcolor='b') + if ax_g: + ax_g.set_ylabel("gas rate (m$^3$/s)", color='r') + ax_g.tick_params(axis='y', labelcolor='r') + if ax_w and ax_g: + ax_w.spines['right'].set_position(('outward', 60)) + + # Combine legends from both axes + lines_1, labels_1 = ax_p.get_legend_handles_labels() + lines_2, labels_2 = ax_w.get_legend_handles_labels() if ax_w else ([], []) + lines_3, labels_3 = ax_g.get_legend_handles_labels() if ax_g else ([], []) + ax_p.legend(lines_1 + lines_2 + lines_3, labels_1 + labels_2 + labels_3, loc=None) + + ax_p.grid(alpha=0.3, lw=0.4) + plt.show() + return fig + +def plot_well_producer(): + return plot_well("PRODUCER.CONTROL") + +def plot_well_water_injector(): + return plot_well("WATER.INJECTOR.CONTROL") + +def plot_well_gas_injector(): + return plot_well("GAS.INJECTOR.CONTROL") + +def get_field_scale(name, path=None): + """ + Get the ``scale`` of a FieldSpecification of the properties file. + + Args: + name (str): Name of the field specification. + path (str, optional): The path to the properties XML file. Defaults to PROPS_XML in case_directory. + + Returns: + float: The scale value. + """ + file_path = path if path else os.path.join(case_directory, PROPS_XML) + for element in ET.parse(file_path).getroot().iter("FieldSpecification"): + if element.attrib.get("name") == name: + return float(element.attrib["scale"]) + raise KeyError(name) + + +def plot_saturation_functions(): + """ + Relative permeabilities and capillary pressures of SAND and CLAY. + + Built from the tables of the input file, no simulation output needed. The + clay capillary curve is a J-function; it is converted to pressure with + J * sigma * sqrt(porosity / permeability), using the surface tension of the + model, the clay porosity and its permeability in the X direction. + + Returns: + matplotlib.figure.Figure: The generated figure. + """ + surface_tension = None + for element in ET.parse(os.path.join(case_directory, BASE_XML)).getroot().iter("JFunctionCapillaryPressure"): + surface_tension = float(element.attrib["wettingNonWettingSurfaceTension"]) + + porosity = get_field_scale("POROSITY.CLAY") + permeability_x = get_field_scale("PERMX.CLAY") + j_function_to_pascals = surface_tension * numpy.sqrt(porosity / permeability_x) + + figure, (axis_1, axis_2) = plt.subplots(1, 2, figsize=(14, 3.6), layout="constrained") + for rock_type, line_style in (("SAND", "-"), ("CLAY", "--")): + sat_water, rel_perm_water = read_table(f"KRW.{rock_type}") + sat_gas, rel_perm_gas = read_table(f"KRG.{rock_type}") + axis_1.plot(sat_water, rel_perm_water, line_style, color="tab:blue", label=f"water, {rock_type}") + axis_1.plot(1.0 - sat_gas, rel_perm_gas, line_style, color="tab:red", label=f"gas, {rock_type}") + sat_water_pc, cap_pressure = read_table(f"PCGW.{rock_type}") + axis_2.plot(sat_water_pc, cap_pressure * (j_function_to_pascals if rock_type == "CLAY" else 1.0) / 1e6, line_style, + color="k", label=rock_type) + axis_1.set_xlabel("water saturation (-)") + axis_1.set_ylabel("relative permeability (-)") + axis_1.legend(loc="upper left", bbox_to_anchor=(1.05, 1)) + axis_2.set_xlabel("water saturation (-)") + axis_2.set_ylabel("gas-water\ncapillary pressure (MPa)") + axis_2.legend(loc="upper left", bbox_to_anchor=(1.05, 1)) + for target_axis in (axis_1, axis_2): + target_axis.set_xlim(0, 1) + target_axis.grid(alpha=0.3, lw=0.4) + return figure + +ALL_FIGURES = ["plot_rock_types", "plot_saturation_functions", + "plot_well_producer", "plot_well_water_injector", "plot_well_gas_injector", + "plot_pressure", "plot_pressure_change", "plot_gas_saturation", + "plot_co2_fraction", "plot_ch4_fraction"] + +def run_command_line_interface(argv=None): + """ + Parse arguments and run the figure generation from the command line. + """ + argument_parser = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + argument_parser.add_argument("figures", nargs="*", help=f"any of {ALL_FIGURES} (default: all)") + argument_parser.add_argument("--hdf5", help="results file (default: %s)" % HDF5_NAME) + argument_parser.add_argument("--outdir", default=".") + argument_parser.add_argument("--dpi", type=int, default=150) + arguments = argument_parser.parse_args(argv) + + if arguments.hdf5: + os.environ["DOME_HDF5"] = arguments.hdf5 + os.makedirs(arguments.outdir, exist_ok=True) + + for figure_name in arguments.figures or ALL_FIGURES: + figure = globals()[figure_name]() + output_path = os.path.join(arguments.outdir, f"{figure_name}.png") + figure.savefig(output_path, dpi=arguments.dpi, bbox_inches="tight") + plt.close(figure) + print("wrote", output_path) + +# The Sphinx plot directive runs this file with __name__ == "__main__", so the +# command-line entry point is disabled when Sphinx is loaded. +if __name__ == "__main__" and "sphinx" not in sys.modules: + import matplotlib + matplotlib.use("Agg") + run_command_line_interface()