Results

reservoir-simulation-energy-transition-application-using-open-source-darts-framework

SPE TrainingMarch, 2023

In this course, participants will learn how to perform a reservoir simulation of various Energy Transition applications using the opensource Delft Advanced Research Terra Simulator (DARTS). The primary focus and developed capabilities of DARTS include a multicomponent multiphase flow of mass and heat with chemical interactions. Advanced multiscale nonlinear formulation and adjoint capabilities improve the run-time performance which makes DARTS a perfect choice for inverse modeling. This workshop will start with a simple 1D homogeneous geothermal model and describe the basic capabilities of the DARTS simulation engine. Next, we will present a realistic heterogeneous model for CCS application with advanced modeling and post-processing capabilities.

SPE Training

application, darts, europe government, Modeling & Simulation, netherlands government, participant, reservoir simulation, social responsibility, sustainability, sustainable development, tu delft, Upstream Oil & Gas, Voskov, Workshop

Country: Europe > Netherlands > South Holland > Delft (0.30)

Genre: Instructional Material > Course Syllabus & Notes (1.00)

Industry: Energy > Oil & Gas > Upstream (1.00)

SPE Disciplines:

Reservoir Description and Dynamics > Reservoir Simulation (0.76)
Data Science & Engineering Analytics > Information Management and Systems (0.74)
Health, Safety, Environment & Sustainability > Sustainability/Social Responsibility > Sustainable development (0.64)
Management > Professionalism, Training, and Education (0.52)

Technology:

Information Technology > Modeling & Simulation (0.64)
Information Technology > Software (0.52)

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A Numerical Study of the Influence of Rock Mineralization on CO2 Storage

Bello, Ayomikun (Skolkovo Institute of Science and Technology, Moscow) | Dorhjie, Desmond Batsa (Skolkovo Institute of Science and Technology, Moscow) | Ivanova, Anastasia (Skolkovo Institute of Science and Technology, Moscow) | Cheremisin, Alexey (Skolkovo Institute of Science and Technology, Moscow)

OnePetroMarch, 2023

Abstract One of the anthropogenic greenhouse gases that significantly affects the climate is CO2, and it may be possible to lower its emission by sequestering it in an appropriate geological subsurface formation. For secure and effective sequestration, it is necessary to answer questions relating to enhancing the reaction rates of rock minerals to speed up sequestration, understanding the critical reservoir parameters involved with geochemically induced changes and how they affect mineralization, and the affinity of rock minerals for dissolution or precipitation in the presence of CO2 and reservoir brine. Therefore, in this paper, a case study of CO2 sequestration in a saline aquifer was investigated by using a numerical simulator to examine the impacts of injection period, temperature, pressure, and salinity on the CO2 mineralization trapping mechanism during the sequestration process. Nine intra-aqueous and seven mineral reactions were modeled to investigate the dissolution and precipitation of formation minerals. The results of this work can provide the appropriate mineralization temperatures at which each of the simulated minerals can either dissolve or precipitate. Calcite and dolomite mineralize more effectively at medium and lower temperatures, despite the fact that the reaction of other minerals such as illite, kaolinite, K-feldspar, and quartz will be more favored at high temperatures. Porosity and pH showed only slight variations, but they were sufficient to show how the dynamics of mineral reactivity and mineralization trapping efficiency had changed.

OnePetro

doi: 10.2118/214045-MS

SPE

SPE-214045-MS

Gas & Oil Technology Showcase and Conference

Country: Europe (0.68)

Genre:

Research Report > New Finding (1.00)
Research Report > Experimental Study (1.00)

Industry: Energy > Oil & Gas > Upstream (1.00)

SPE Disciplines:

Reservoir Description and Dynamics > Storage Reservoir Engineering > CO2 capture and sequestration (1.00)
Reservoir Description and Dynamics > Reservoir Simulation (1.00)
Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
(4 more...)

Technology: Information Technology > Modeling & Simulation (0.68)

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Well Optimisation Technique with Backward Elimination Process for Greenfield Gas Development

Vardcharragosad, Pichit (PTTEP) | Doungprasertsuk, Chonlada (PTTEP) | Srisuriyon, Jantakan (PTTEP) | Hamdan, Mohamad Kamal (PTTEP)

OnePetroMarch, 2023

Abstract The well optimization technique with backward elimination aims to determine the optimum number of wells and their locations that can maximise project value and its recoverable resources, through repeated ranking of candidate wells and eliminating the poorest performer. For a greenfield development, subsurface uncertainties are typically still very large due to limited data from exploration and appraisal wells. This study outlines our approach to perform well optimization with these governing uncertainties in order to support the decision-making process. First, multiple realizations of reservoir models are constructed to represent range of possible outcomes by sampling different values from uncertainty parameters. Backward elimination for well optimization is then performed on those realizations. Wells can be ranked based on means and standard deviations of their performance, and the lowest rank candidate is eliminated from the process one at a time. At this point, project economic and resources are evaluated to find optimum set of wells for field development. Furthermore, well performance data from multiple realization models are carefully analyzed to define key subsurface uncertainties that need to be managed. Solution from this backward elimination with subsurface uncertainty workflow can maximize project valuation because it balances the risk of overspending to drill sub-optimum wells in some realizations with the risk of losing sell opportunity due to insufficient field deliverability in the other realizations. Development decision will be more robust because it is based on the optimum configuration that is applicable irrespective of the unknown uncertain quantities. Moreover, detailed analysis on well performance data allows us to better understand the risk associated with our planned wells so that appropriate de-risking plan can be developed and combined into development strategy. The backward elimination process is straightforward to implement and normally does not require very high computational expense. Thus, it is suitable to be used with uncertainty workflow with multiple realizations of reservoir models, which will increase computational requirement by multiple times. Other commonly used techniques for well optimization such as a Genetic Algorithm (GA) or an Evolutionary Algorithm (EA) are computational expensive by themselves already; and they will require even more runtime when using them with this uncertainty workflow. This paper extends backward elimination approach for well optimization to be used with uncertainty workflow. The overall uncertainty analysis workflow is discussed and provided, with key steps detailing the approach taken. Project valuation and recoverable resources can be further optimized with this new approach, and ultimately can guide the decision making in field development.

OnePetro

doi: 10.2523/IPTC-22973-EA

IPTC

IPTC-22973-EA

International Petroleum Technology Conference

Country:

Europe (1.00)
Asia > Middle East (0.69)
North America > United States (0.68)

Industry: Energy > Oil & Gas > Upstream (1.00)

SPE Disciplines:

Well Drilling > Drilling Operations > Drilling optimization (1.00)
Reservoir Description and Dynamics > Reservoir Simulation > Evaluation of uncertainties (1.00)
Production and Well Operations > Well Operations and Optimization (1.00)
(2 more...)

Technology:

Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (0.89)
Information Technology > Artificial Intelligence > Machine Learning > Evolutionary Systems (0.70)

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Jasmine Field, Gulf of Thailand, Fill and Spill History Across Faults: A Fault-Seal Calibration

OnePetroMarch, 2023

Abstract Compartmentalization of reservoirs in operating fields is commonly caused by sealing of faults (Cerveny et al., 2004; Davies and Handschy, 2003;Davies et al., 2019; Knipe, 1992; Yielding et al., 1997; Yielding et al., 2010). Calibrating this seal, however, is difficult without adequate subsurface data. A local region across the central part of the Jasmine Field, Jasmine A, along the northern extent of the Pattani basin in the Gulf of Thailand, was selected in this study for detailed fault-seal analysis calibration. The objective was to present the details of the fill and spill history from a juxtaposition analysis across the faults. The large number of well penetrations with fluid and lithofacies data and the 3D models of mapped permeability distribution provided a subsurface framework to reduce the uncertainty and allow a more comprehensive analysis of the crossfault reservoir juxtaposition and fluid contact levels. Crossfault flow behavior and fill and spill history were evaluated by examining fluid contacts in a strike view of the fault, with the properties juxtaposed. The Jasmine Field is a narrow structural high that is cut by many NE-SW and NNW-NNE trending faults forming fault-bounded compartments. Reservoirs in the field are typically thin, stacked high-permeability fluvial sandstones of primarily Miocene age separated by thin shale beds that occur over a depth range of several thousand meters. Many of the sands have unique hydrocarbon-water contacts of oil or gas and water. Reservoir juxtaposition across the faults suggests that fault seal plays a major role in the trap. By comparing fluid contacts in each fault block, cases with different contacts across the fault likely represent a fault membrane seal. Contacts occurring at the same height suggest crossfault leakage. The evaluation was done by estimating permeability distributions across the fault. These results, however, were not adequately determined simply from the fluid contacts on either side of the fault: fill histories in adjacent fault blocks and lateral structural controls also had to be accounted for. The results together allowed a unique fill and spill history to be defined. The results of the juxtaposition analysis for the main faults bounding the local structural trap in Jasmine A provided a calibration for a 3D analysis of the faults, including estimation of fault-rock properties.

OnePetro

doi: 10.2523/IPTC-22827-MS

IPTC

IPTC-22827-MS

International Petroleum Technology Conference

Country:

Europe > United Kingdom > North Sea > Central North Sea (1.00)
Asia > Thailand > Gulf of Thailand (1.00)

Genre: Research Report > New Finding (0.34)

Geologic Time: Phanerozoic > Cenozoic > Neogene > Miocene (0.34)

Geophysics: Geophysics > Seismic Surveying (0.68)

Industry: Energy > Oil & Gas > Upstream (1.00)

Oilfield Places:

Asia > Vietnam > Gulf of Thailand > Pattani Basin (0.99)
Asia > Thailand > Gulf of Thailand > Western Basin (0.99)
Asia > Thailand > Gulf of Thailand > Pattani Basin > Jasmine/Ben Yen Field > Jasmine Field (0.99)
(4 more...)

SPE Disciplines:

Reservoir Description and Dynamics > Reservoir Simulation (1.00)
Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
Reservoir Description and Dynamics > Reservoir Characterization > Faults and fracture characterization (1.00)
Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)

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An Intelligent Safe Well Bottom-Hole Pressure Monitoring of CO2 Injection Well into Deep Saline: A coupled Hydro-Mechanical Approach

Tariq, Zeeshan (King Abdullah University of Science and Technology) | Yan, Bicheng (King Abdullah University of Science and Technology) | Sun, Shuyu (King Abdullah University of Science and Technology)

OnePetroFebruary, 2023

Abstract Geological Carbon Sequestration (GCS) in deep geological formations, like saline aquifers and depleted oil and gas reservoirs, brings enormous potential for large-scale storage of carbon dioxide (CO2). The successful implementation of GCS requires a comprehensive risk assessment of the confinement of plumes and storage potential at each storage site. To better understand the integrity of the caprock after injecting CO2, it is necessary to develop robust and fast tools to evaluate the safe CO2 injection duration. This study applied deep learning (DL) techniques, such as fully connected neural networks, to predict the safe injection duration. A physics-based numerical reservoir simulator was used to simulate the movement of CO2 for 170 years following a 30-year CO2 injection period into a deep saline aquifer. The uncertainty variables were utilized, including petrophysical properties such as porosity and permeability, reservoir physical parameters such as temperature, salinity, thickness, and operational decision parameters such as injection rate and perforation depth. As mentioned earlier, the reservoir model was sampled using the Latin-Hypercube sampling approach to account for a wide range of parameters. Seven hundred twenty-two reservoir simulations were performed to create training, testing, and validation datasets. The DNN model was trained, and several executions were performed to arrive at the best model. After multiple realizations and function evaluations, the predicted results revealed that the three-layer FCNN model with thirty neurons in each layer could predict the safe injection duration of CO2 into deep saline formations. The DNN model showed an excellent prediction efficiency with the highest coefficient of determination factor of above 0.98 and AAPE of less than 1%. Also, the trained predictive models showed excellent agreement between the simulated ground truth and predicted trapping index, yet 300 times more computationally efficient than the latter. These findings indicate that the DNN-based model can support the numerical simulation as an alternative to a robust predictive tool for estimating the performance of CO2 in the subsurface and help monitor the storage potential at each part of the GCS project.

OnePetro

doi: 10.2118/213315-MS

SPE

SPE-213315-MS

Middle East Oil, Gas and Geosciences Show

Country:

Asia > Middle East (0.94)
Europe > Denmark > North Sea (0.28)

Genre: Research Report (0.47)

Geophysics: Geophysics > Seismic Surveying (0.46)

Industry: Energy > Oil & Gas > Upstream (1.00)

Oilfield Places:

Europe > Denmark > North Sea > Danish Sector > Central Graben > Block 5604/29 > South Arne Field (0.99)
Asia > India > Andhra Pradesh > Bay of Bengal > Krishna-Godavari Basin > Ravva Block > Ravva Field (0.99)

SPE Disciplines:

Reservoir Description and Dynamics > Storage Reservoir Engineering > CO2 capture and sequestration (1.00)
Reservoir Description and Dynamics > Reservoir Simulation (1.00)
Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
(8 more...)

Technology:

Information Technology > Modeling & Simulation (1.00)
Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.89)

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Application of Image Processing Techniques in Deep-Learning Workflow to Predict CO2 Storage in Highly Heterogeneous Naturally Fractured Reservoirs: A Discrete Fracture Network Approach

Tariq, Zeeshan (King Abdullah University of Science and Technology) | Yan, Bicheng (King Abdullah University of Science and Technology) | Sun, Shuyu (King Abdullah University of Science and Technology)

OnePetroFebruary, 2023

Abstract Naturally fractured reservoirs (NFRs), such as fractured carbonate reservoirs, are commonly located worldwide and have the potential to be good sources of long-term storage of carbon dioxide (CO2). The numerical reservoir simulation models are an excellent source for evaluating the likelihood and comprehending the physics underlying behind the interaction of CO2 and brine in subsurface formations. For various reasons, including the rock's highly fractured and heterogeneous nature, the rapid spread of the CO2 plume in the fractured network, and the high capillary contrast between matrix and fractures, simulating fluid flow behavior in NFR reservoirs during CO2 injection is computationally expensive and cumbersome. This paper presents a deep-learning approach to capture the spatial and temporal dynamics of CO2 saturation plumes during the injection and monitoring periods of Geological Carbon Sequestration (GCS) sequestration in NFRs. To achieve our purpose, we have first built a base case physics-based numerical simulation model to simulate the process of CO2 injection in naturally fractured deep saline aquifers. A standalone package was coded to couple the discrete fracture network in a fully compositional numerical simulation model. Then the base case reservoir model was sampled using the Latin-Hypercube approach to account for a wide range of petrophysical, geological, reservoir, and decision parameters. These samples generated a massive physics-informed database of around 900 cases that provides a sufficient training dataset for the DL model. The performance of the DL model was improved by applying multiple filters, including the Median, Sato, Hessian, Sobel, and Meijering filters. The average absolute percentage error (AAPE), root mean square error (RMSE), Structural similarity index metric (SSIM), peak signal-to-noise ratio (PSNR), and coefficient of determination (R) were used as error metrics to examine the performance of the surrogate DL models. The developed workflow showed superior performance by giving AAPE less than 5% and R more than 0.94 between ground truth and predicted values. The proposed DL-based surrogate model can be used as a quick assessment tool to evaluate the long-term feasibility of CO2 movement in a fracture carbonate medium.

OnePetro

doi: 10.2118/213359-MS

SPE

SPE-213359-MS

Middle East Oil, Gas and Geosciences Show

Country:

Asia > Middle East (0.68)
Europe > Denmark > North Sea (0.28)

Geophysics: Geophysics (0.68)

Industry: Energy > Oil & Gas > Upstream (1.00)

Oilfield Places: Europe > Denmark > North Sea > Danish Sector > Central Graben > Block 5604/29 > South Arne Field (0.99)

SPE Disciplines:

Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Naturally-fractured reservoirs (1.00)
Reservoir Description and Dynamics > Storage Reservoir Engineering > CO2 capture and sequestration (1.00)
Reservoir Description and Dynamics > Reservoir Simulation (1.00)
Data Science & Engineering Analytics > Information Management and Systems (1.00)

Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)

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Accurate Two-Phase Flow Simulation in Faulted Reservoirs by Combining Two-Point Flux Approximation and Mimetic Finite Difference Methods

Dong, Rencheng (The University of Texas at Austin (Corresponding author)) | O. Alpak, Faruk (Shell International Exploration and Production Inc.) | F. Wheeler, Mary (The University of Texas at Austin)

OnePetroFebruary, 2023

Summary Faulted reservoirs are commonly modeled by corner-point grids (CPGs). Because the two-point flux approximation (TPFA) method is not consistent on non-K-orthogonal grids, multi-phase flow simulation using TPFA on CPGs may have significant discretization errors if grids are not K-orthogonal. We developed a novel method to improve the simulation accuracy where the faults are modeled by polyhedral cells, and mimetic finite difference (MFD) methods are used to solve flow equations. We use a cut-cell approach to build the mesh for faulted reservoirs. A regular K-orthogonal grid is first constructed, and then cells are divided where fault planes are present. Most cells remain K-orthogonal while irregular non-K-orthogonal polyhedral cells can be formed with multiple cell divisions. We investigated three spatial discretization methods for solving the pressure equation on general polyhedral grids, including the TPFA, MFD, and TPFA-MFD hybrid methods. In the TPFA-MFD hybrid method, the MFD method is only applied to the part of the domain with severe grid non-K-orthogonality, while the TPFA method is applied to the rest of the domain. We compare flux accuracy between TPFA and MFD methods by solving a single-phase flow problem. The reference solution is obtained on a rectangular grid, while the same problem is solved by TPFA and MFD methods on a grid with non-K-orthogonal cells near a fault. Fluxes computed using TPFA exhibit larger errors in the vicinity of the fault, while fluxes computed using MFD are still as accurate as the reference solution. We also compare saturation accuracy for two-phase (oil and water) flow in faulted reservoirs when the pressure equation is solved by different discretization methods. Compared with the reference saturation solution, saturation exhibits non-physical errors near the fault when the pressure equation is solved by the TPFA method. Because the MFD method yields accurate fluxes over general polyhedral grids, the resulting saturation solutions agree with reference saturation solutions with an enhanced accuracy when the pressure equation is solved by the MFD method. Based on the results of our simulation studies, we observe that the accuracy of the TPFA-MFD hybrid method is very close to the accuracy of the MFD method, while the TPFA-MFD hybrid method is computationally cheaper than the MFD method.

OnePetro

doi: 10.2118/206298-PA

SPE

SPE-206298-PA

SPE Journal

Country:

Europe (0.93)
North America > United States > Texas (0.46)
Asia > Middle East > UAE (0.28)

Geophysics: Geophysics > Seismic Surveying (0.46)

Industry: Energy > Oil & Gas > Upstream (1.00)

SPE Disciplines:

Reservoir Description and Dynamics > Reservoir Simulation (1.00)
Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Multiphase flow (1.00)
Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
(2 more...)

Technology: Information Technology > Modeling & Simulation (1.00)

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Progress Toward Pilot-Scale Simulation of In-Situ Combustion Incorporating Geomechanics

Li, Y. (Energy Resources Engineering, Stanford University) | Manrique, E. J. (Ecopetrol) | Kovscek, A. R. (Energy Resources Engineering, Stanford University (Corresponding author))

OnePetroFebruary, 2023

Summary In-situ combustion (ISC) is a promising thermal enhanced oil recovery method with benefits for deep reservoirs, potentially lesser energy requirements as compared to steam injection, and low opportunity cost. Although successful ISC projects have been developed all over the world, challenges still exist including difficulties in monitoring combustion-front progress in the field, describing multiscale physical processes, characterizing crude oil kinetics fully, and simulating ISC at field scale. This work predicts combustion front propagation and the effect of thermally induced stress at the scale of an ISC pilot project. Reservoir deformation was characterized by a geomechanical model to investigate the correlation of combustion front progress with reservoir and surface deformation. We upscaled the reaction kinetics directly from combustion tube experiments and calibrated the laboratory-scale model compared with experimental measurements. We then upscaled numerical simulation to a 3D geometry incorporating a geomechanical model. The change in scale is significant as the combustion tube is 6.56 ft (2 m) in length, whereas the dimensions of the 3D model are 1,440 ft by 1,440 ft (439 m) by 1,400 ft (427 m). The elastic properties were defined by Young’s modulus and Poisson’s ratio, whereas the plastic properties were defined by a Mohr-Coulomb model. A sensitivity study examined the reliability of the model, showing the reaction progress and geomechanical responses were not significantly impacted by gridblock dimensions and reservoir heterogeneity. Finally, a field-scale model was developed covering an area of 5,960 ft (1817 m) by 4,200 ft (1280 m). We observed successful ISC simulation including ignition as air injection started. The temperature increased immediately to more than 800°C (1,400°F) based on the chemical kinetics implemented. The temperature history indicated that the combustion front propagated from the injection well into the reservoir with an average velocity of 0.16 ft/D (0.049 m/d). A surface deformation map correlated with the progress of ISC in the subsurface. Land surface uplift because of ISC ranges from 0.1 ft (0.03 m) to several feet, depending on the rock properties and subsurface events. This proof-of-concept model indicated strong potential to detect the surface movement using interferometric synthetic aperture radar (InSAR) and/or tiltmeters to monitor dynamically combustion front positions in subsurface.

OnePetro

doi: 10.2118/212266-PA

SPE

SPE-212266-PA

SPE Reservoir Evaluation & Engineering

Country:

Europe (1.00)
North America > United States > California (0.46)
North America > United States > Oklahoma (0.28)
(2 more...)

Geophysics: Geophysics > Seismic Surveying (0.69)

Industry: Energy > Oil & Gas > Upstream (1.00)

Oilfield Places:

South America > Colombia > Meta Department > Llanos Basin > Chichimene Field (0.99)
North America > United States > Louisiana > Sabine Uplift > Bellevue Field (0.99)
Asia > India > Gujarat > Cambay Basin > Balol Field (0.99)

SPE Disciplines:

Reservoir Description and Dynamics > Reservoir Simulation > Scaling methods (1.00)
Reservoir Description and Dynamics > Reservoir Characterization > Reservoir geomechanics (1.00)
Reservoir Description and Dynamics > Improved and Enhanced Recovery > Thermal methods (1.00)

Technology: Information Technology > Modeling & Simulation (1.00)

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An Extended Unified Viscoelastic Model for Predicting Polymer Apparent Viscosity at Different Shear Rates

Zeynalli, Mursal (Petroleum Engineering Department, Khalifa University of Science and Technology, SAN Campus (Corresponding author)) | Al-Shalabi, Emad Walid (Petroleum Engineering Department, Khalifa University of Science and Technology, SAN Campus) | AlAmeri, Waleed (Petroleum Engineering Department, Khalifa University of Science and Technology, SAN Campus)

OnePetroFebruary, 2023

Summary Polymer flooding is one of the most commonly used chemical enhanced oil recovery (EOR) methods. Conventionally, this technique was believed to improve macroscopic sweep efficiency by sweeping only bypassed oil. Nevertheless, recently it has been found that polymers exhibiting viscoelastic behavior in the porous medium can also improve microscopic displacement efficiency resulting in higher additional oil recovery. Therefore, an accurate prediction of the complex rheological response of polymers in porous media is crucial to obtain a proper estimation of incremental oil to polymer flooding. In this paper, a novel viscoelastic model is proposed to comprehensively analyze the polymer rheological behavior in porous media. This proposed model was developed and validated using 30 coreflooding tests obtained from the literature and further verified against a few existing viscoelastic models. The proposed viscoelastic model is considered an extension of the unified apparent viscosity model provided in the literature and is termed as extended unified viscoelastic model (E-UVM). The main advantage of the proposed model is its ability to capture the polymer mechanical degradation at ultimate shear rates primarily observed near wellbores. Moreover, the fitting parameters used in the model were correlated to rock and polymer properties using machine learning technique, significantly reducing the need for time-consuming coreflooding tests for future polymer screening works. Furthermore, the E-UVM was implemented in MATLAB Reservoir Simulation Toolbox (MRST) and verified against the original shear model existing in the simulator. It is worth mentioning that the irreversible viscosity drop for mechanical degradation regime was captured during implementing our model in the simulator. It was found that implementing the E-UVM in MRST for polymer non-Newtonian behavior might be more practical than the original method. In addition, the comparison between various viscosity models proposed earlier and E-UVM in the reservoir simulator showed that the latter model could yield more reliable oil recovery predictions as the apparent viscosity is modeled properly in the mechanical degradation regime, unlike UVM or Carreau models. This study presents a novel viscoelastic model that is more comprehensive and representative as opposed to other models in the literature. Furthermore, the need to conduct an extensive coreflooding experiment can be reduced by virtue of developed correlations that may be used to estimate model fitting parameters accounting for shear-thickening and mechanical degradation.

OnePetro

doi: 10.2118/206010-PA

SPE

SPE-206010-PA

SPE Reservoir Evaluation & Engineering

Country:

Europe (1.00)
North America > United States > Texas (0.93)
Asia > Middle East > UAE (0.68)
South America (0.67)

Genre:

Research Report > New Finding (0.68)
Overview > Innovation (0.66)

Industry: Energy > Oil & Gas > Upstream (1.00)

Oilfield Places:

Europe > Austria > Vienna Basin > Matzen Field (0.99)
Asia > China > Heilongjiang > Songliao Basin > Daqing Field > Yian Formation (0.99)
Asia > China > Heilongjiang > Songliao Basin > Daqing Field > Mingshui Formation (0.99)
North America > United States > Washington > Sultan Basin (0.93)

SPE Disciplines:

Well Drilling > Drilling Fluids and Materials > Drilling fluid selection and formulation (chemistry, properties) (1.00)
Reservoir Description and Dynamics > Reservoir Simulation > Scaling methods (1.00)
Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
(2 more...)

Technology:

Information Technology > Modeling & Simulation (1.00)
Information Technology > Artificial Intelligence > Machine Learning (1.00)

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Inverse Modeling of Reservoirs with Tilted Fluid Contacts

OnePetroFebruary, 2023

Summary In this paper, we present a new approach for simulating reservoirs with tilted fluid contacts produced by hydrodynamics. The proposed method solves a nonlinear inverse problem to determine the aquifer flow field that best reproduces the observed contact tilt. The computational effort required to solve this inverse problem is reduced by choosing a pressure-based objective function and applying gradient-based optimization. This approach is entirely automated, in contrast to previous works that have used laborious trial-and-error methods to estimate the aquifer flow field. In addition, the proposed method introduces no additional physics beyond hydrodynamics to model reservoirs with tilted contacts. The proposed method is integrated into a parallel reservoir simulator. A synthetic reservoir is constructed by introducing an artificial tilt, and the new approach is applied to estimate the aquifer flow field. The estimate produced by the proposed method matches the true flow field well and is able to prevent large fluid motions near the contact surface when simulating production from the reservoir. The proposed method is compared with an existing approach that uses capillary pressure adjustments to hold the tilted contact in place. The proposed method is shown to outperform the existing approach without significantly impacting the simulation results.

OnePetro

doi: 10.2118/210587-PA

SPE

SPE-210587-PA

SPE Journal

Country:

Europe (1.00)
Asia > Middle East (1.00)
North America > Canada (0.93)
North America > United States > Texas > Dawson County (0.24)

Genre: Overview > Innovation (0.68)

Geophysics: Geophysics > Seismic Surveying (0.46)

Industry: Energy > Oil & Gas > Upstream (1.00)

Oilfield Places:

North America > United States > California > Ventura Basin (0.99)
Europe > Norway > Norwegian Sea > Møre Basin > PL 442 > Block 6305/8 > Ormen Lange Field > Springar Formation (0.99)
Europe > Norway > Norwegian Sea > Møre Basin > PL 442 > Block 6305/8 > Ormen Lange Field > Egga Formation (0.99)
(30 more...)

SPE Disciplines:

Reservoir Description and Dynamics > Reservoir Simulation (1.00)
Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
Reservoir Description and Dynamics > Reservoir Characterization (1.00)
Data Science & Engineering Analytics > Information Management and Systems (1.00)

Technology:

Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (1.00)
Information Technology > Artificial Intelligence > Machine Learning (0.93)
Information Technology > Modeling & Simulation (0.89)

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Collaborating Authors

reservoir-simulation-energy-transition-application-using-open-source-darts-framework

A Numerical Study of the Influence of Rock Mineralization on CO2 Storage

Well Optimisation Technique with Backward Elimination Process for Greenfield Gas Development

Jasmine Field, Gulf of Thailand, Fill and Spill History Across Faults: A Fault-Seal Calibration

An Intelligent Safe Well Bottom-Hole Pressure Monitoring of CO2 Injection Well into Deep Saline: A coupled Hydro-Mechanical Approach

Application of Image Processing Techniques in Deep-Learning Workflow to Predict CO2 Storage in Highly Heterogeneous Naturally Fractured Reservoirs: A Discrete Fracture Network Approach

Accurate Two-Phase Flow Simulation in Faulted Reservoirs by Combining Two-Point Flux Approximation and Mimetic Finite Difference Methods

Progress Toward Pilot-Scale Simulation of In-Situ Combustion Incorporating Geomechanics

An Extended Unified Viscoelastic Model for Predicting Polymer Apparent Viscosity at Different Shear Rates

Inverse Modeling of Reservoirs with Tilted Fluid Contacts

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