WO2007076044A2 - Method, system and program storage device for reservoir simulation utilizing heavy oil solution gas drive - Google Patents
Method, system and program storage device for reservoir simulation utilizing heavy oil solution gas drive Download PDFInfo
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- 238000004088 simulation Methods 0.000 title claims abstract description 52
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Classifications
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B47/00—Survey of boreholes or wells
- E21B47/10—Locating fluid leaks, intrusions or movements
Definitions
- the present invention relates generally to methods and systems for reservoir simulation predicting the flow of fluids in an underground reservoir, and more particularly, to enhancing reservoir performance forecasting by accounting for fluid flow effects due to heavy oil solution gas drive.
- Reservoir simulation is used to predict the flow of fluids in an underground reservoir.
- the fluid flow may include oil, gas and water.
- Such reservoir forecasting is important in reservoir management and estimating the potential recovery from a reservoir.
- a reservoir is selected for which the rock and fluid properties are to be modeled and simulated.
- the reservoir is modeled and discretized into a plurality of cells.
- Nonlinear governing equations are constructed for each cell, generally in the form of finite difference equations, which are representative of properties of rocks and fluids in the reservoir.
- rock properties include porosity, capillary pressure, and relative permeability for each phase of fluid (oil, water, gas.)
- fluid properties include oil viscosity, oil formation factor (Bo), and pressure, temperature, and saturation in each of the cells.
- Nonlinear terms in these equations are linearized to arrive at a set of linear equations for each timestep of the simulation.
- Absolute permeability K is a measure of a rock's ability to transmit flow and can vary greatly throughout a reservoir and surrounding formations. When gas, oil and water move through porous rock, they do not move at equal velocities. Rather, the fluids compete with one another.
- Relative permeability, k r is the ratio of the effective permeability, k e , when more than one fluid is present, to the absolute permeability K.
- Effective permeability k e is the measured permeability of a porous medium to one fluid when another is present.
- the relationship between relative permeability k r and saturation S depends on the reservoir rock and fluid and may vary between formations. Also, the relative permeability K depends on the relative proportion of the fluids present, i.e. fluid saturations.
- FIG. 1 illustrates a typical relative permeability k rg versus saturation curve for gas. Gas cannot flow at any appreciable rate until gas saturation reaches a minimum threshold value. Looking to FIG. 1, this threshold value is referred to as critical gas saturation S° c and begins at a value of approximately 0.03 or about 3% saturation. At the other end of the curve is an endpoint relative permeability k ⁇ , ro which is the gas relative permeability value k rg at which movement of residual oil remaining in the rock is minimal. As reservoir rock will always contain a minimal amount of residual oil, gas saturation cannot reach 100%. The total percentage of saturation must add up to 100%. In this case, there is a maximum 76% gas saturation S g and 24 % residual oil saturation . As seen in FIG. 1.
- relative permeability curves are developed through laboratory experiments on core samples taken from reservoirs for which reservoir simulation is to be performed. For example, displacement tests may be used to develop the relative permeability k r gvs. saturation S g curves. Such tests are well known. Particularly well known displacement test procedures are described in E.F. Johnson, D.P. Bossier, and V.O. Naumann, Calculations of Relative Permeability from Displacement Experiments, Trans. Am. Inst. Mining Engineers, Volume 216, 1959, pp. 370-378 and S. C. Jones and W.O. Roszelle, Graphical Techniques for Determining Relative Permeability from Displacement Experiments, Journal of Petroleum Engineering, Volume 30, pp. 807-817 (1978). These displacements experiments are usually conducted at slow depletion rates as it is commonly accepted that permeability curves are generally independent of how fast gas flows through reservoir rock.
- the relative permeability k rg versus saturation S g curves can be theoreticially created.
- the curves may be developed from comparable analogue reservoirs.
- the relative permeabilities k rg to be used in a reservoir simulation can simply be obtained from these curves assuming saturations S g in the cells of the reservoir model are known.
- the saturations S g are generally known either from initial conditions established at the beginning of a simulation, from the last timestep in the simulation or else from calculations within an iteration in a timestep.
- Heavy oil may be considered to include oil having an API gravity 20° or less. Significant quantities of gas are often entrained within the heavy oil while under high reservoir pressures. After sufficient production of heavy oil from a reservoir, the pressure in portions of the reservoir may drop below the bubble point pressure. At this pressure, gas readily comes out of solution from the heavy oil. Once sufficient gas has been released from the oil, the gas is believed to form a continuous phase and gas can flow through the reservoir and the rate of production of gas is significantly enhanced. As indicated above, the saturation S 1 , at which there is an initiation of gas flow is referred to as the critical gas saturation or S ⁇ .
- FIG. 1 1 shows a graph of cumulative gas produced from a core sample versus time in minutes. The breakpoint in the curve shown there represents S gc .
- a method of predicting a property of at least one fluid in a subterranean reservoir containing heavy oil entrained with gas is disclosed.
- the property might include the overall production of fluids from the reservoir, i.e., oil, gas and water.
- the prediction is made using a reservoir simulator which uses a reservoir model having a plurality of cells representative of the reservoir. For at least some of the cells and for at least some of the iterations of the reservoir simulation, gas relative permeability k rg is dependent upon the local fluid velocities v o in the cells.
- a baseline correlation is developed for gas relative permeability k rg versus gas saturation S g , typically based on displacements tests performed at slow depletion rates.
- a capillary number N ca dependent correlation is developed between at least one of, and most preferably, both of critical gas saturation S ge and capillary number N ca and endpoint of gas relative permeability K rgro and capillary number N ca .
- Non-limiting examples of how this correlation may be expressed include, by way of example and not limitation, using a mathematical equation which describes a curve or by creating a corresponding look-up table.
- Capillary numbers N 0 are calculated for a plurality of cells in the reservoir model representative of the subterranean reservoir for which fluid properties are to be simulated.
- S gc and/or k rgm values are selected from the capillary number dependent correlations based upon the capillary numbers N e calculated for the cells.
- Adjusted baseline correlations are then developed. For example, the original endpoints of the baseline curve, i.e.
- FIG. 2 suggests that an adjusted baseline curve can be developed by changing the original endpoint values S° c and k r ° firn to other values of S ⁇ 0 and k rRm which are based, in part, upon the velocity of oil V n flow through the cells.
- Gas relative permeabilities k rg for the plurality of cells are selected from corresponding adjusted baseline correlations. These relative permeabilities k rg are then used in a reservoir simulation to predict a property of at least one fluid in a subterranean reservoir containing heavy oil entrained with gas. This predicted property may be the production of oil, water or gas.
- This predicted property may be the production of oil, water or gas.
- the current adjusted baseline correlation for that cell is fixed for the remaining simulation time-steps. This fixing of the adjusted baseline correlation once gas begins to flow assists in maintaining stability during the solution of the system of equations modeling the reservoir.
- One or both of the capillary dependent correlations of S gc or k rgro can be used in adjusting the baseline correlation to come up with an adjusted baseline correlation.
- These adjusted baseline correlations through the use of the capillary numbers N 0 , capture the effects that the depletion rate/fluid velocity flow and viscosity have on relative permeability during heavy oil production under heavy oil solution gas drive.
- depletion experiments are performed at various depletion rates to develop the capillary number dependent correlations for the S gc and k rgro .
- Relative permeabilities k rg can be selected which are dependent upon capillary numbers N c calculated at the beginning of a time step in a reservoir simulation.
- the capillary numbers N c can be repeatedly calculated throughout iterations in a timestep to provide constant updating of relative permeability curves during the simulation. Again, this updating of a capillary number N c for relative permeability curves of a cell is preferably stopped once the saturation S in a cell remains at or above the critical gas saturation S gc during simulation.
- capillary number N ca dependent correlations can be used in conjunction with a reservoir model, and calculated capillary numbers N 0 calculated during a reservoir simulation, to more accurately estimate relative permeabilities k fg to be used in the reservoir simulation of heavy oil.
- FIG. 1 shows a conventional gas relative permeability k rg versus saturation S g curve
- FIG. 2 depicts adjusting the conventional curve of FIG. 1 by modifying the endpoints of S° c and k ⁇ m to coincide with values of S gc and k rm ⁇ selected from capillary number dependent correlations of S gc versus N ca and k rgro versus N ca ;
- FIG. 3 shows a flowchart of steps taken in a preferred embodiment of the present invention for carrying out reservoir simulation which utilizes gas relative permeabilities k rg which are dependent upon local velocities v,, of fluid flow in cells;
- FIG. 4 shows a schematic drawing of an experimental setup used to determine gas saturation S g from core and sandpack samples
- FIG. 5 depicts a graph of average sandpack pressure and pressure differential versus time across a sandpack sample in a fast depletion experiment
- FIG. 6 illustrates a graph of cumulative oil and gas produced in the fast depletion experiment of FIG. 5;
- FIG. 7 shows a graph of average sandpack sample pressure and effluent density for a slow depletion experiment
- FIG. 8 depicts a graph of average sandpack sample pressure and cumulative oil produced for a slow depletion experiment
- FIG. 9 shows a graph of oil recovery as a function of average pore pressure for sandpack experiments at depletion rates of 0.3 and 0.03 cc/min.
- FIG. 10 is a graph of oil recovery as a function of average pore pressure for core experiments at depletion rates of 0.082, 0.08, and 0.002 cc/min, respectively;
- FIG. 11 is a graph of cumulative gas produced (measured) and cumulative solution gas produced (calculated) vs. time. DETAILED DESCRIPTION OF THE INVENTION
- the present invention accounts for the effects of heavy oil solution gas drive, and more particularly, for the effects that the rates of fluid depletion have on heavy oil production.
- Velocity or depletion rate dependent relative permeability values k rg are utilized in a heavy oil reservoir simulation to provide for more accurate reservoir simulation forecasts than are achieved with conventional reservoir simulation.
- capillary numbers N c which are dependent on oil velocities v o , are calculated for reservoir cells. These capillary numbers N 1 are used to adjust baseline relative permeability correlations to account for the velocity or depletion rate effects on relative permeability k rg .
- capillary number N ca dependent critical gas saturations S gc and/or endpoint relative permeabilities k rgro correlations are first developed, preferably based on laboratory experiments. Then values of S gc and/or k rgro , corresponding to the capillary number N e calculated for a cell, are used to adjust the baseline relative permeability correlation for that cell. Relative permeability k rg values are then selected from these capillary number adjusted baseline relative permeability correlations based upon the saturations S g in the cells.
- FIG. 3 provides an exemplary flowchart of steps which may be used to implement the heavy oil solution gas drive reservoir simulation of the present invention.
- step 100 a baseline correlation is created between k rg and S g . Correlations are then developed between S ⁇ c and N co and/or k r!ir ⁇ > and JV ⁇ in step 1 10.
- capillary numbers TV 0 are calculated in step 120.
- adjusted baseline correlations between k rg and S g are established in step 130 which Eire dependent upon N c and the correlations developed in step 110.
- Gas relative permeabilities k rg are then selected in step 140 for each of the cells from the adjusted baseline correlations between k rg and S g using saturation S g values from the cells. These capillary number dependent permeabilities k rg axe then used in step 150 in a reservoir simulation to predict properties of fluid flow in the reservoir model.
- Correlations between gas relative permeability k rg and saturation S g are established so that relative permeability values k rg can be utilized by a reservoir simulator based upon known saturations values S g in cells of a reservoir model.
- these correlations are experimentally developed from core samples from the reservoir for which the reservoir simulation is to be performed.
- representative sand packs and/or synthetic oil may also be used to develop the correlations.
- the preferred methods to establish these baseline correlations are the methods of Johnson, Bossier, and Naumann or else the method Jones and Roszell, which were cited above in the background section and are well known to those skilled in establishing permeability curves.
- FIG. 1 is an exemplary baseline curve or correlation of gas relative permeability k rg versus saturation S g .
- a baseline value for S° c is shown at about 0.03 or 3%.
- S gc and N ca and between k rgro and N ca were obtained by curve fitting the S gc , k rgra and N ca data. History matching of production data on the core samples may be used to enhance the accuracy of the correlations.
- Live oil was prepared by combining unfiltered dead oil and methane. The water content of the oil was negligible.
- PVT Pressure, Volume, and Temperature
- R s Gas-Oil-Ratio
- B 0 Oil Formation Volume Factor
- B g Gas Formation Volume Factors
- FIG. 4 shows a schematic of the experimental set-up. During the depletion, the pressure (inlet, outlet, and at several points along the core), the production of oil and gas, and the density of the effluent was monitored. The coreholder was placed in a Siemens Somatom HiQ CT scanner to monitor spatial and temporal gas saturation.
- the dry sandpack was initially CT (Computer Tomography) scanned at reservoir conditions (i.e., under overburden stress and at temperature). The core was then flushed with CO 2 , evacuated and saturated with kerosene at a back pressure of ⁇ 1600 psia. The sandpack (or composite core) permeability was measured with kerosene at several flow rates. The kerosene-saturated sandpack was also CT-scanned. The sandpack porosity was calculated using the wet and dry CT-scans and CT number of air and kerosene. Live oil was then slowly injected into the core to displace the kerosene. Permeability of the sandpack was also measured with live oil at several flow rates. The live-oil injection rate was then reduced so that the differential pressure across the core was less than 2 psi.
- CT Computer Tomography
- the live-oil saturated sandpack was CT-scanned to record initial conditions.
- Depletion was started at a pressure of -1500-1700 psia (about 150-350 psi above the bubble point pressure).
- the inlet valve was closed and the downstream Isco pump A was operated at a constant withdrawal rate.
- the pumps were switched and Isco pump B withdrew fluids while Isco pump A delivered oil and gas into the collection system.
- the pump cycle was repeated until the outlet pressure decreased to about 200 psia.
- Pressures, temperatures and fluid accumulation in the collection system were continuously recorded using conventional data acquisition software.
- the density of the produced fluid was continuously measured using an in-line density meter.
- the sandpack was also periodically scanned to determine directly gas saturation, S g , as a function of time and position. 4.
- a Siemens Somatom HiQ CT scanner was used to monitor spatial and temporal gas saturation.
- This third generation CT-scanner has 768 stationary detectors and a rotating X-ray source. Scans were conducted at 133 kV and the scan time was 2.7 seconds. The voxel size was approximately 0.625 mm 3 for a scan thickness of 10 mm and the uncertainty in saturation measurement was +/- 1.5 saturation units. Scan thicknesses of 10 mm and/or 5 mm were acquired.
- FIGS. 5 and 6 show ⁇ the average sandpack pressure and pressure differential across the sandpack during a fast depletion experiment.
- FIG. 6 illustrates the cumulative oil and gas produced during a fast depletion experiment.
- FIGS. 7 and 8 show typical responses which were observed with this instrument.
- FIG. 7 illustrates the average sandpack pressure and effluent density for a slow depletion experiment.
- FIG. 8 depicts the average sandpack pressure, cumulative oil produced (collected in the separator and inferred based on the effluent density) for a slow depletion experiment.
- the critical gas saturation S gL is the saturation at which the cumulative gas produced starts to increase significantly.
- FIG. 11 shows the cumulative gas produced (measured) and cumulative solution gas produced (calculated) vs. time.
- the critical gas saturation S ge can also be determined based on the effluent density.
- Methods 2 and 3 require the use of PVT data (namely formation volume factor and density as a function of pressure).
- N is the oil in place (stb) at the beginning of the experiment and at pressure P h N p is the cumulative oil produced (stb) at pressure P (N p is measured with the collection system)
- B 0 and B ot are the oil formation volume factors at P and P 1 , respectively and c/is the rock or sandpack compressibility (1/psi).
- N p (c 0 + c f ) x (P i - P) ⁇ x N (3)
- the sandpack and composite core compressibility are calculated using Eqn. (3).
- N p is measured through the collection system.
- the amount of oil produced can be based on the effluent density, p eff :
- N n depletion _ rate x — — + N ⁇ (5)
- CT sa ⁇ U rated_core is the CT number for the sandpack saturated with kerosene (at initial pressure), and is the CT number of the sandpack saturated with gas.
- CT / i g and CT gas are the CT numbers for kerosene and air, respectively.
- CT P is the CT number measured during the depletion (at pressure -P)
- CT s atu rate d c or e is the CT number for the sandpack saturated with live oil (at initial pressure)
- CT d , ⁇ _ core is the CT number of the sandpack saturated with air and at initial pressure.
- the average capillary number (N ca ) was calculated using the pressure differential recorded during the depletion.
- the capillary number can be calculated in several ways. In this preferred embodiment, the following formula was used:
- K is the permeability of the core or sandpack
- ⁇ is the gas-oil surface tension (estimated to be 80 dyn/cm for the oil used in the experiment)
- L is the sandpack length
- AP is the pressure differential observed before the gas is becomes mobile.
- S ⁇ 1 is plotted as a function of7V ⁇ for all the available experiments.
- the data is then curve fit, preferably, using an exponential function (Eqn. (9)) to interpolate/extrapolate the missing data.
- Eqn. (9) an exponential function
- S gC and k ⁇ r0 are "conventional" critical gas saturation and end-point of gas relative permeability values, respectively, as described above in the background and as shown in FIG. 1.
- Reservoir simulations conducted on core samples at various depletion rates are used to determine the values for k rgm .
- the critical gas saturation 5 1 ⁇ is known, so this endpoint on a gas relative permeability k rg versus saturation S g is known.
- Various estimates are made for the other endpoint of the curve k .
- a trial and error method is then used to determine which estimated value of k rgro matches the experimental production output from the core sample at a particular depletion rate.
- This history matching of experimental production results with simulated runs is used to determine k rgro at a number of depletion rates, which correspond to N ca values.
- S gc and k rg ⁇ vs. N ca are implemented in this exemplary embodiment, preferably, using a modified implicit algorithm in a reservoir simulator.
- the preferred forms for S gc and k rgro are input as functions of N ca using Eqns. (9) and (10) from above.
- the parameters a, b, c and d are user's input to the reservoir simulator.
- S gc is a function of a, b, and capillary number N ca .
- k rgm is a function of c, d, and N ca .
- the calculated S ⁇ c and k values are limited to user's specified maximums and minimums, respectively.
- N c is directional, S gc and k rsro are calculated for each cell face and thus are directional too.
- a modified implicit algorithm of the preferred embodiment is implemented to calculate S gc and k rgro .
- S gc and k rgro are calculated, for example, using Eqns. (9) and (10), respectively.
- S and k rgro become invariant — neither increase nor decrease.
- Their values are calculated using the capillary number N 0 at the beginning of the time-step when the gas-phase becomes mobile and fixed for all remaining time-steps.
- a modified expression for capillary number N c is preferably incorporated into the reservoir simulator using the following expression:
- ⁇ og oil-gas interfacial tension
- K rock permeability
- ⁇ o oil-phase potential
- P 0 the change in pressure across a face of a cell
- p 0 density of oil
- g gravitational constant
- D change in depth from a datum.
- N c is ideally computed implicitly, this greatly simplifies the calculation of derivatives for gas relative permeability ⁇ k rg ) as a function of primary variables during Jacobian generation.
- the potential gradient in the N 0 calculation is directional and is based on the gradient across the face of the two adjacent grid blocks. For each Newton iteration, a capillary number N c is calculated for each grid-block face. In a 3-D model, there will be six directional N 0 for each grid block. Each N c corresponds to one of the six values at the cell faces. The use of directional N 0 results in a Jacobian that can be easily solved by conventional linear equation solvers. For wells, in this preferred embodiment, an averaged N c from all grid-block faces is calculated.
- Each cell is assigned a particular rock type or facies. Each of these rock types or facies corresponds to particular baseline gas relative permeability k rg vs. saturation S g curve, such as the one shown in FlG. 1. These respective baseline curves are adjusted for each respective cell. This is accomplished for each cell by replacing the original values of S° c and k r ° gm with capillary number dependent values of S gc and k rgr ⁇ l calculated using Eqns. (9) and (10) and the particular capillary number N c calculated for each respective cell.
- the baseline curve connecting these endpoints is preferably adjusted by scaling. Scaling of the relative permeability could be done using several methods. Equation (12) shows such a method:
- K rg F( s g g - s s ⁇ c - ) (12)
- the function F could be (but is not limited to) a simple power law:
- S gc in Eqn. 12 or 13 is equal to S gc .
- S gc in Eqns. 12 and 13 is now a function of the capillary number.
- Saturation values S 2 may come from initial conditions when the reservoir simulation is first started, from the previous time step, or else from values calculated during iterations within a time step.
- the saturation S ⁇ of each reservoir cell is then examined and the corresponding relative permeability k rg is selected from the adjusted baseline correlation. As described above, if S g ⁇ S ⁇ 1 . , then the correlation from the previously calculated curve is used to determine k rg ,
- Finite difference equations are solved to determine unknowns, such as pressure P or saturation S g . These finite difference equations rely upon the latest updated relative permeabilities k r , including the capillary number dependent gas permeabilites k rg for the reservoir cells. Such finite difference equations are well known those skilled in the art of reservoir simulation. Examples of well known solution methods for such equations include: (1) Fully Explicit; (2) Implicit Pressure, Explicit Saturation (IMPES); (3) folly Implicit; (4) Sequential Implicit (SEQ), Adaptive Implicit (AIM); and Cascade. In the preferred embodiment, a fully Implicit method is used to solve these equations.
- the present invention also include a system for carrying out the above reservoir simulation using relative permeabilities k rg that are dependent upon depletion rate/fluid velocity and viscosities of crude oil. Further, the present invention also includes a program storage device which carries instructions for carrying out this reservoir simulation using fluid velocity dependent relative permeabilities.
- CTdry_ C ore CT number of a sample saturated with gas
- CT salurale d_ core CT number of a sample saturated with kerosene (at initial pressure);
- CTp CT number measured during depletion at pressure P
- CT u q CT number for kerosene
- CTg a s CT number for air
- d exponent for calculating k rgm ;
- N c capillary number calculated for a particular cell of a reservoir model
- N ca capillary number
- R s Gas-to-oil ratio
- S saturation, dimensionless
- S 0 oil saturation, dimensionless;
- S° c endpoint critical gas saturation, dimensionless;
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BRPI0620170-9A BRPI0620170A2 (en) | 2005-12-22 | 2006-12-20 | method and system for predicting a property of at least one fluid in an underground reservoir, method for simulating heavy oil flow in an underground reservoir, and program storage device containing instructions for performing a reservoir simulation method |
CA2634757A CA2634757C (en) | 2005-12-22 | 2006-12-20 | Method, system and program storage device for reservoir simulation utilizing heavy oil solution gas drive |
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Cited By (10)
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US8849640B2 (en) | 2008-11-06 | 2014-09-30 | Exxonmobil Upstream Research Company | System and method for planning a drilling operation |
WO2016164507A1 (en) * | 2015-04-09 | 2016-10-13 | Schlumberger Technology Corporation | Oilfield reservior saturation and permeability modeling |
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US8849640B2 (en) | 2008-11-06 | 2014-09-30 | Exxonmobil Upstream Research Company | System and method for planning a drilling operation |
US10584570B2 (en) | 2013-06-10 | 2020-03-10 | Exxonmobil Upstream Research Company | Interactively planning a well site |
CN104481523B (en) * | 2014-11-11 | 2018-07-13 | 中国石油天然气股份有限公司 | Viscous crude dissolved gas drive develops experimental system for simulating and method |
WO2016164507A1 (en) * | 2015-04-09 | 2016-10-13 | Schlumberger Technology Corporation | Oilfield reservior saturation and permeability modeling |
US10787902B2 (en) | 2015-06-01 | 2020-09-29 | Schlumberger Technology Corporation | Method and system for correcting a capillary pressure curve |
US11066905B2 (en) | 2015-06-30 | 2021-07-20 | Schlumberger Technology Corporation | Oilfield reservoir saturation and permeability modeling |
CN107944599A (en) * | 2017-10-31 | 2018-04-20 | 中国石油天然气股份有限公司 | The Forecasting Methodology of oil gas horizontal well production |
CN107944599B (en) * | 2017-10-31 | 2020-10-09 | 中国石油天然气股份有限公司 | Method for predicting yield of oil-gas horizontal well |
US11492895B2 (en) | 2018-11-13 | 2022-11-08 | Saudi Arabian Oil Company | Relative permeability ratio from wellbore drilling data |
CN110671084A (en) * | 2019-09-04 | 2020-01-10 | 中国石油化工股份有限公司 | Method for simulating flooding numerical value of common thick oil water-soluble viscosity reducer |
CN110863806A (en) * | 2019-11-28 | 2020-03-06 | 西安石油大学 | Carbon dioxide flooding gas front dynamic change prediction method |
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CA2634757C (en) | 2014-12-16 |
CN101366041A (en) | 2009-02-11 |
BRPI0620170A2 (en) | 2011-11-01 |
CN101366041B (en) | 2011-10-12 |
CA2634757A1 (en) | 2007-07-05 |
WO2007076044A3 (en) | 2007-12-27 |
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