Reservoir souring forecasting

Abstract

A method for modeling reservoir souring using object-oriented numerical solutions separate from reservoir topography is described. Specifically, flow physics are separated into one or more objects, along with one or more H.sub.2S generation mechanisms, for modeling on time and spatial scales separate from field scale modeling.

Claims

1. A simulation method of a hydrocarbon field development configuration, comprising: a) providing a computer having one or more parallel graphics processing unit (GPU); b) providing historical data for a hydrocarbon field; c) inputting, into said GPU, the network topography, field layout, fluid description, and reservoir characteristics of one or more field configurations to create a field model of said hydrocarbon field; d) utilizing object-oriented software, on said GPU, for dividing fluid flow physics models of a reservior field configuration the flow physics and H.sub.2S generation mechanisms of one or more fluids for said one or more field configurations into a plurality of individual basic elements of appropriate units for individual calculation so that the behavior of each individual basic element can be analyzed separately, wherein at least one of said basic elements incorporates said historical data; e) defining one or more time scales and/or one or more spatial levels for simulating one or more individual basic elements; f) simulating H.sub.2S generation and transport and fluid flow for at least one field model and one or more individual basic elements over said time scales and/or spatial level; and, g) displaying the results of said simulating step.

2. The method of claim 1, further comprising h) comparing simulation results for one or more field configurations; i) selecting the configuration with the lowest H.sub.2S generation; and j) creating and implementing a final field development plan to maximize hydrocarbon recovery from said hydrocarbon field.

3. The method of claim 1, where said individual basic elements contain network parameters such as location, connectivity, size, boundary conditions, fluid dynamics, thermodynamics, chemical reactions, heat transfer rates, or combinations thereof.

4. The method of claim 1, where said H.sub.2S generation mechanisms are biological reactions of sulfate reducing bacteria (SRB), natural scavenging reactions, thermal reactions, interaction between H.sub.2S and hydrocarbons, reactions between H.sub.2S and water injection, reactions between H.sub.2S and production equipment, and/or a combination thereof.

5. The method of claim 1, wherein said reservoir characterization data includes initial conditions & composition, geometry, flow area, column, porosity, bulk density, permeability, imbibition and drainage relative permeabilities, skill friction, Sorw, capillary pressures, reactive substrates descriptions or a combination thereof.

6. The method of claim 1, wherein at least one individual basic element is a hierarchical object incorporating elements from another individual basic element.

7. The method of claim 1, wherein at least one individual basic element is a hierarchical object incorporating elements from another individual basic element and wherein said hierarchical object is a Reservoir Element.

8. The method of claim 1, wherein said spatial levels are pore, core and field levels.

9. The method of claim 1, wherein said fluid injections comprise seawater injections, freshwater injections, waste water injections, produced water re-injection, brine water injection, and mixtures thereof.

10. The method of claim 1, further comprising inputting a second network topography and a second field layout for a second reservoir field configuration.

11. A non-transitory machine-readable storage medium, which when executed by at least one processor of a computer, perform the method of claim 1.

12. A computer-implemented method of modeling souring within a reservoir, said computer having at least one parallel graphics processing unit (GPU), comprising: a) dividing a reservoir topology into a finite number of grid cells that forms a grid of the reservoir; b) defining interaction regions contained within adjacent grid cells in the grid; c) inputting, onto said GPU, a description of a field layout for said reservoir topology, and historical data for said reservoir; d) utilizing object-oriented software, on said GPU, for dividing the flow physics and H.sub.2S generation mechanisms for one or more fluids injections for each of said interaction regions into a plurality of individual basic elements of appropriate units for individual calculation so that the behavior of each individual basic element can be analyzed separately; e) defining one or more time scales and/or one or more spatial scales; f) performing, using the computer, H.sub.2S generation and transport forecast operations for said fluid injection for each interaction region for said time scales and said spatial scales using a plurality of said individual basic elements and said field layout and said historical data; and g) outputting the model of souring.

13. The method of claim 12, further comprising h) comparing simulation results for one or more field configurations; i) selecting the configuration with the lowest H.sub.2S generation; and j) creating and implementing a final field development plan to maximize hydrocarbon production from said reservoir.

14. The method of claim 12, where said individual basic elements contain network parameters such as location, connectivity, size, boundary conditions, fluid dynamics, thermodynamics, chemical reactions, heat transfer rates, or combinations thereof.

15. The method of claim 12, where said H.sub.2S generation mechanisms are biological reactions of sulfate reducing bacteria (SRB), natural scavenging reactions, thermal reactions, interaction between H.sub.2S and hydrocarbons, reactions between H.sub.2S and water injection, reactions between H.sub.2S and production equipment, and/or a combination thereof.

16. The method of claim 12, wherein said reservoir characterization data includes initial conditions & composition, geometry, flow area, column, porosity, bulk density, permeability, imbibition and drainage relative permeabilities, skill friction, Sorw, capillary pressures, reactive substrates descriptions or a combination thereof.

17. The method of claim 12, wherein at least one individual basic element is a hierarchical object incorporating elements from another individual basic element.

18. The method of claim 12, wherein at least one individual basic element is a hierarchical object incorporating elements from another individual basic element and wherein said hierarchical object is a Reservoir Element.

19. The method of claim 12, wherein said spatial levels are pore, core and field levels.

20. The method of claim 12, wherein said fluid injections comprise seawater injections, freshwater injections, waste water injections, produced water re-injection, brine water injection, and mixtures thereof.

21. The method of claim 12, further comprising inputting a second network topography and a second field layout for a second reservoir field configuration.

22. A non-transitory machine-readable storage medium, which when executed by at least one processor of a computer, performs one or more steps of the method of claim 12.

23. A method for forecasting reservoir souring for a field development configuration, comprising: a) providing a computer having at least one parallel graphics processing unit (GPU) and a graphical user interface (GUI) display; b) inputting, on said GPU, the network topography, field layout, historical reservoir data and the fluid flow physics of a reservoir field configuration, wherein said fluid is freshwater, seawater and/or a combination thereof; c) modeling said flow physics to form at least one flow physics model, wherein at least one model incorporates said historical reservoir data; d) encapsulating said at least one flow physics model as a first object, e) representing one or more predetermined souring mechanisms as a second object; f) displaying said network topography, field layout and said objects on said GUI display; g) applying at least one first object and at least one second object to said network topography; and, h) calculating the reservoir souring for said reservoir field configuration.

24. The method of claim 23, further comprising i) comparing simulation results for one or more field configurations; j) selecting the configuration with the lowest H.sub.2S generation; and k) creating and executing a final field development plan to maximize hydrocarbon production.

25. The method of claim 23, where said individual basic elements contain network parameters such as location, connectivity, size, boundary conditions, fluid dynamics, thermodynamics, chemical reactions, heat transfer rates, or combinations thereof.

26. The method of claim 23, where said H.sub.2S generation mechanisms are biological reactions of sulfate reducing bacteria (SRB), natural scavenging reactions, thermal reactions, interaction between H.sub.2S and hydrocarbons, reactions between H.sub.2S and water injection, reactions between H.sub.2S and production equipment, and/or a combination thereof.

27. The method of claim 23, wherein said reservoir characterization data includes initial conditions & composition, geometry, flow area, column, porosity, bulk density, permeability, imbibition and drainage relative permeabilities, skill friction, Sorw, capillary pressures, reactive substrates descriptions or a combination thereof.

28. The method of claim 23, wherein at least one individual basic element is a hierarchical object incorporating elements from another individual basic element.

29. The method of claim 23, wherein at least one individual basic element is a hierarchical object incorporating elements from another individual basic element and wherein said hierarchical object is a Reservoir Element.

30. The method of claim 23, wherein said spatial levels are pore, core and field levels.

31. The method of claim 23, wherein said fluid injections comprise mixtures of seawater injections, freshwater injections, waste water injections, produced water re-injection, and brine water injection.

32. The method of claim 23, further comprising inputting a second network topography and a second field layout for a second reservoir field configuration.

33. A non-transitory machine-readable storage medium, which when executed by at least one processor of a computer, performs one or more steps of the method of claim 23.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1A Sulfate reduction to H.sub.2S by the SRB.

(2) FIG. 1B. Reservoir souring by SRB.

(3) FIG. 2. Depiction of object orientated model according to one embodiment.

(4) FIG. 3. Simple field layout for forecasting bio-generated souring.

(5) FIG. 4. Model flow.

DESCRIPTION OF EMBODIMENTS OF THE DISCLOSURE

(6) The disclosure provides a novel approach to reservoir souring forecasting using an object-oriented model. This type of model allows the user to run calculations and detailed forecasting analysis for different mechanism having differing temporal and spatial scales while preserving the model's larger scale physics and field development configuration.

(7) The prediction and forecasting of H.sub.2S concentrations provided by the currently described systems will improve project cost, operability, safety, and commercial issues.

(8) The present methods includes any of the following embodiments in any combination(s) of one or more thereof:

(9) In one embodiment, a simulation of a hydrocarbon field development is provided where a computer having one or more parallel graphics processing unit (GPU); process historical data for a hydrocarbon field; by inputting, into said GPU, the network topography, field layout, fluid description, and reservoir characteristics of one or more field configurations to create a field model; utilizing object-oriented software, on said GPU, for dividing the flow physics and H2S generation mechanisms of one or more fluids for said one or more field configurations into a plurality of individual basic elements of appropriate units for individual calculation so that the behavior of each individual basic element can be analyzed separately, wherein at least one of said basic elements incorporates said historical data; defining one or more time scales and/or one or more spatial levels for simulating one or more individual basic elements; simulating H2S generation and transport and fluid flow for at least one field model and one or more individual basic elements over said time scales and/or spatial level; and, displaying the results of said simulating step.

(10) In another embodiment, souring is modeled within a reservoir on a computer having at least one parallel graphics processing unit (GPU), by dividing a reservoir topology into a finite number of grid cells that forms a grid of the reservoir; defining interaction regions contained within adjacent grid cells in the grid; inputting, onto said GPU, a description of a field layout for said reservoir topology, and historical data for said reservoir; utilizing object-oriented software, on said GPU, for dividing the flow physics and H2S generation mechanisms for one or more fluids injections for each of said interaction regions into a plurality of individual basic elements of appropriate units for individual calculation so that the behavior of each individual basic element can be analyzed separately; defining one or more time scales and/or one or more spatial scales; performing, using the computer, H2S generation and transport forecast operations for said fluid injection for each interaction region for said time scales and said spatial scales using a plurality of said individual basic elements and said field layout and said historical data; and outputting the model of souring.

(11) In an additional embodiment, reservoir souring for a field development configuration is forecast by providing a computer having at least one parallel graphics processing unit (GPU) and a graphical user interface (GUI) display; inputting, on said GPU, the network topography, field layout, historical reservoir data and the fluid flow physics of a reservoir field configuration, wherein said fluid is freshwater, seawater and/or a combination thereof; modeling said flow physics to form at least one flow physics model, wherein at least one model incorporates said historical reservoir data; encapsulating said at least one flow physics model as a first object, representing one or more predetermined souring mechanisms as an second object; displaying said network topography, field layout and said objects on said GUI display; applying at least one first object and at least one second object to said network topography; and, calculating the reservoir souring for said reservoir field configuration.

(12) In some embodiments, simulation results for one or more field configurations are compared; the configuration with the lowest H.sub.2S generation is selected; and field development plan can be developed. Individual basic elements may contain network parameters such as location, connectivity, size, boundary conditions, fluid dynamics, thermodynamics, chemical reactions, heat transfer rates, or combinations thereof. H.sub.2S generation mechanisms include biological reactions of sulfate reducing bacteria (SRB), natural scavenging reactions, thermal reactions, interaction between H.sub.2S and hydrocarbons, reactions between H.sub.2S and water injection, reactions between H.sub.2S and production equipment, and combinations. Reservoir characterization data may includes initial conditions & composition, geometry, flow area, column, porosity, bulk density, permeability, imbibition and drainage relative permeabilities, skill friction, Sorw, capillary pressures, reactive substrates descriptions or a combination thereof. Hierarchical objects may be incorporated as basic elements from other individual basic element, including a Reservoir Element. Spatial levels may be pore, core and field levels.

(13) Fluid injections may include seawater injections, freshwater injections, waste water injections, produced water re-injection, brine water injection, and mixtures thereof.

(14) A second network topography and a second field layout for a second reservoir field configuration may be incorporated.

(15) A non-transitory machine-readable storage medium, which when executed by at least one processor of a computer, may performs one or more steps of the methods described herein.

(16) Simulation of reservoir models requires solution of equations that govern conservation of mass and energy over time. The process of simulation involves solving the equations over discrete time intervals to monitor changes in reservoir properties. The equations incorporate transport, phase behavior, and reaction relationships from the petrophysical and fluid models. Spatial variations in reservoir properties require the equations to be spatially discretized in a way that corresponds to the grid geometry and topology. Time dependent terms require temporal discretization to monitor the accumulation of mass or energy at grid node locations throughout the reservoir. Spatial discretization methods are selected to ensure accurate representation of grid property heterogeneities.

(17) Previous solutions to modeling H.sub.2S production used ad hoc proprietary models that hardwire the particular reservoir configuration and development pattern of the field into the measurements. This type of technique relies on an operator's expectation on what mechanisms are dominant and then optimizes the model to the identified dominant mechanism. However, such models do not work for alternate field configurations or account for changes in relative important H.sub.2S mechanisms.

(18) Other solutions include piggy-backing a H.sub.2S model on a commercial reservoir simulator, wherein the simulator is designed mainly for detailed flow predictions for short time horizons. Yet others do not even attempt to forecast H.sub.2S and prefer to rely on productions rates, then incorporate mitigation and workovers.

(19) None of these approaches is satisfactory.

(20) An object-oriented approach to forecasting H.sub.2S production is disclosed herein. In object-oriented modeling, blocks of code are assembled like puzzle pieces into larger components. The modeling technique described herein separates flow physics and H.sub.2S generation and transport mechanisms from the reservoir network topology and encapsulates them as reusable objects. This encapsulation programming style allows selective hiding of properties and methods in an object by building an impenetrable wall to protect the code from accidental corruption. Other benefits of encapsulation are a reduction in system complexity, and thus increased robustness, by allowing the developer to limit the inter-dependencies between software components.

(21) The encapsulated objects are reusable and they can be paired with many different field development configurations. This results in an adaptable prediction tool for the different configurations and for the modeling of many mechanisms over various time scales throughout the production cycle. Thus, the encapsulated models can be extended without compromising the fidelities of previous forecast or solutions.

(22) Each encapsulated object can address one aspect of the system such as chemical mechanisms, biochemical mechanisms, boundary conditions, thermodynamic calculations, production profiles, bottom-hole conditions, optimization calculations and the like. For a given model, only the necessary objects will be used. This allows the same objects to be used over multiple system layouts and to easily be applied to field development configurations as field conditions are modified.

(23) Many features are needed to meet the application areas for forecasting bio-generated reservoir souring. These include a simple description of field layout (injectors, producers, separators); representative time profile of flow rates; time-based flow model, biochemistry mechanism/kinetics; fluid description; rigorous thermodynamics and H.sub.2S partitions; parameter calibration; and flexibility, as well as modular, efficient computation.

(24) The present method is exemplified with respect to the tests below, however, this is exemplary only, and the method can be broadly applied to any reservoir design. The following examples are intended to be illustrative only, and not unduly limit the scope of the appended claims.

Simple Reservoir Model

(25) A model was developed to test the ability to forecast bio-generated souring, H.sub.2S field production rate, souring mitigation, metallurgy selection, export gas/fuel gas specification, and indigenous H.sub.2S/thermal generation using a simple reservoir design and the COPRISM software linked with GUTS.

(26) By combining COPRISM with the object oriented method herein, additional information such as forecasting H.sub.2S field production rate, souring mitigation design, metallurgy selection, exportation of gas/fuel gas specifications, and indigenous H.sub.2S and thermal generation forecasting can be obtained.

(27) In this test, a bio-generated reservoir souring forecast model was developed. The model started with a simple description of the field layout and added objects as needed. The field layout for the sample reservoir, shown in FIG. 3, involves only injectors, producers, and separators. The design is flexible enough to meet all of the above application areas for COPRISM and object-oriented modeling. However, this test focuses on the bio-generated reservoir souring forecast.

(28) The flow network of this reservoir was removed from the physics. Thus, changes can be made to the model as needed without hindering the underlying calculations for the H.sub.2S generation.

(29) The objects were created as described above. For the bio-generated reservoir souring forecast, the necessary features include, but are not limited to, a representative time profile of flow rates, a time-based flow model, biochemical mechanism and/or kinetics, fluid descriptions, rigorous thermodynamics/H.sub.2S partition, and parameter calibration. Each of these features are in separate objects to allow for flexible, modular and efficient computation.

(30) As seen in FIG. 3, the model also incorporated three reservoir elements.

(31) This field layout was uploaded onto a computer running both COPRISM and GUTS. From there, objects can be added. In this particular test, the model objects test are a Thermodynamics/PVT object, Boundary Condition objects [State (Temp/Pres.) BC, flow rate BC (material stream)], a Reservoir object, a Separator object, a Mixer object, a Chemistry object, and Simulation objects [Adjust/Target, calibrate functions].

(32) The thermodynamics object performed equilibrium calculations using GUTS. This will allow for a simplified reservoir composition to be used when estimating the partitioning of the H.sub.2S between oil, gas, water and/or biofilms. For instance, wax, asphaltenes, and hydrates were not included in the composition input in the present test because they are not expected to affect H.sub.2S partitioning.

(33) The Boundary Condition objects (BC) contain information for the state boundary conditions or the flow rate boundary conditions at one or more nodes. The state BC object sets the pressure, temperature and composition at each node and may be used to model well bottom hole pressure. The flow rate BC object sets the flow rate and composition at each node and may also be used to model the injection well.

(34) The Separator object performs a three-phase T-P flash calculation using GUTS to determine the concentration of H.sub.2S in the live crude, free gas, and water phases in the reservoir.

(35) Hardware may preferably include massively parallel and distributed Linux clusters, which utilize both CPU and GPU architectures. Alternatively, the hardware may use a LINUX OS, XML universal interface run with supercomputing facilities provided by Linux Networx, including the next-generation Clusterworx Advanced cluster management system.

(36) Another system is the Microsoft Windows 7 Enterprise or Ultimate Edition (64-bit, SP1) with Dual quad-core or hex-core processor, 64 GB RAM memory with Fast rotational speed hard disk (10,000-15,000 rpm) or solid state drive (300 GB) with NVIDIA Quadro K5000 graphics card and multiple high resolution monitors, which we normally use with Gedco's Vista processing package.

(37) Slower systems could be used but are less preferred since processing and imaging may already be compute intensive.

(38) All of the references cited herein are expressly incorporated by reference. The discussion of any reference is not an admission that it is prior art to the present invention, especially any reference that may have a publication data after the priority date of this application. Incorporated references are listed again here for convenience: 1. Coombe, et al., Simulation of Bacterial Souring Control in an Albertan Heavy Oil Reservoir, 10th Canadian International Petroleum Conference (the 60th Annual Technical Meeting of the Petroleum Society), June 16-18, in Calgary, Alberta (2009). 2. Farhadinia, Predictive Modeling of Reservoir Souring, CPGE, University of Texas at Austin, Feb. 2, 2006. 3. Haghshenas, Mehdi, Modeling and Remediation of Reservoir Souring, Ph.D. Thesis, The University of Texas at Austin (2011). 4. Lambo, et al. Competitive, microbially mediated reduction of nitrate with sulfide and aromatic oil components in a low-temperature, Western Canadian oil reservoir. Environ. Sci. Technol. 42: 88941-8946 (2008). 5. NACE 06661: Burger, et al., Forecasting the effect of produced water reinjection on reservoir souring in the Ekofisk field, Corrosion 2006, National Association of Corrosion Engineers' 61st Annual Conference and Exhibition, March 12-16, San Diego, Calif. (2006). 6. SPE 93297: Burger, et al., A mechanistic model to evaluate reservoir souring in the Ekofisk field, SPE International Symposium on Oilfield Chemistry, Houston, Tex., 2-4 Feb. 2005. 7. SPE 121432: Burger & Jenneman, Forecasting the effects of reservoir souring from waterflooding a formation containing siderite, SPE International Symposium on Oilfield Chemistry, The Woodlands, Tex., 20-22 Apr. 2009. 8. SPE 132346: Zuluaga, et al. Technical Evaluations to Support the Decision to Reinject Produced Water, SPE Annual Technical Conference and Exhibition, Florence, Italy, 20-22 Sep. 2010. 9. SPE 164067: Burger, et al., On the partitioning of hydrogen sulfide in oilfield systems, SPE International Symposium on Oilfield Chemistry, The Woodlands, Tex., Apr. 8-10, 2013. 10. SPE 164068: Burger, et al., The impact of dissolved organic carbon type on the extent of reservoir souring., SPE International Symposium on Oilfield Chemistry, The Woodlands, Tex., Apr. 8-10, 2013. 11. SPE 173722: Burger, E. D., et al., Injection of Nitrate During PWRI to Reduce H.sub.2S Production in a Bohai Bay Oil Field Offshore China (2015).