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Collaborating Authors
Williston Basin
Summary Long-term (multiyear) buildup tests conducted for multifractured horizontal wells (MFHWs) completed in shale reservoirs offer the unique opportunity to study and analyze flow-regimes sequences that are not commonly observed with typical buildup test periods. In this study, two buildup periods (including a rarely observed, nearly 5-year buildup), and the preceding extended flow tests, were analyzed for an MFHW completed in an Australian shale gas reservoir within the Beetaloo Basin. The objectives of the analyses were to (a) identify the sequence of flow regimes observed for each test (flow/buildup, F/BU) period; (b) extract estimates of reservoir permeability and hydraulic fracture properties; and (c) study the evolution of these properties with each subsequent test. An MFHW, the Amungee NW-1H, completed in the Velkerri B shale in Australia, was analyzed. Due to a casing deformation and inability to mill out plugs beyond this, most of the flow contribution was from the heel stages of the well. The first F/BU period was conducted from 2016 to 2021 (a nearly 5-year buildup), while the second F/BU was initiated in 2021 (buildup is currently continuing). The extended (>1 month) production tests (EPTs) preceding the buildups were analyzed using rate-transient analysis (RTA) methods [flow-regime identification/straightline /type curve analysis (TCA)] modified for shale gas properties (e.g., desorption), while the buildups were analyzed using classic pressure-transient analysis (PTA) methods. The first (~5-year) buildup period (BU 1) revealed a sequence of bilinear-linear-elliptical-pseudoradial flow followed by a second linear flow period. The first two flow regimes are interpreted to be associated with interfracture flow, while the latter is assumed to correspond to linear flow to the well. Elliptical/radial flow around fractures is rationalized to occur due to interpreted relatively short fracture half-lengths (corresponding to the high-conductivity portion of the fractures). Permeability estimates are in good agreement with diagnostic fracture injection test (DFIT) analysis. Flow-regime interpretations for the other test periods (EPTs 1 and 2, BU 2) are largely consistent, although EPT 1 flow-regime interpretation was challenged by noisy data. Permeability values derived from EPTs 1 and 2 are smaller than from buildup tests, suggesting stress sensitivity caused by drawdown. Properties derived from the analysis of BU 1 and 2 are in good agreement, suggesting that any effects caused by stress sensitivity of reservoir parameters are largely reversible. Permeability derived from all tests are much larger than those obtained from laboratory data, leading to the interpretation that natural fractures are elevating system permeability. Fracture half-lengths are also much shorter than those typically reported for MFHWs. The mostly “textbook” quality well test data obtained for this field example, combined with the length of the test periods, resulted in one of the most complete flow-regime sequences observed for an MFHW completed in a shale gas reservoir. The existence of a radial flow period observed for all test periods (interpreted to be interfracture radial flow) allows for confident estimates of reservoir permeability/skin and their evolution with each subsequent test, which is rarely reported. The radial-flow-derived permeability, combined with early linear flow analysis, also allowed fracture half-length to be estimated for all tests. This case study adds significantly to our understanding of shale gas reservoir characteristics and flow-regime sequences associated with MFHWs.
- North America > Canada (0.94)
- North America > United States > Texas (0.94)
- Oceania > Australia > Northern Territory (0.66)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geophysics > Borehole Geophysics (0.68)
- Geophysics > Seismic Surveying (0.46)
- Oceania > Australia > Northern Territory > Beetaloo Basin > Beetaloo Extension Basin > EP 98 > Kalata South Field > Kalata South 1 Well (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- North America > United States > South Dakota > Williston Basin > Bakken Shale Formation (0.99)
- (5 more...)
Reconciliation and Insights from a Holistic Reservoir Characterisation Program in a Late, Early-Oil to Early, Peak-Oil Window Shale Oil Play - Eromanga Basin, Australia
Richards, Brenton (Origin Energy) | Solano, Nisael (University of Calgary) | Baruch, Elizabeth (Tamboran Resources) | Gordon, John (University of Calgary) | Younis, Adnan (University of Calgary) | DeBuhr, Chris (University of Calgary) | Ghanizadeh, Amin (University of Calgary) | Stasiuk, Lavern (Stasiuk Petrography) | Bein, Cassandra (Origin Energy) | Mitchell, Brendon (Oceania Geo) | Clarkson, Christopher R. (University of Calgary) | Pedersen, Per (University of Calgary)
Abstract The objective of this work was to develop and apply integrated geological and experimental workflows to enable a holistic evaluation of the reservoir quality and potential producibility of a prospective shale oil play - the Toolebuc Formation (Eromanga Basin), Australia. Tight oil reservoirs are notoriously difficult to characterize; routine analytical and experimental methods developed for tight reservoir characterisation are prone to providing contradicting observations depending on the complexity of the reservoir. This paper explores the data collection methods and results from a calcareous, organic-rich shale and demonstrates the benefits of combing multiple analytical techniques in the early stages of resource appraisal. The Toolebuc Formation is within a late early oil to early peak oil window at the key well sites which, provide access to the most thermogenically mature material recovered for testing in the play to date. Routine shale core analysis data indicate significant gas-filled porosity, which is inconsistent with the anticipated fluid profiles for the optically determined thermal maturity window. Isotopic data collected on mud gas during drilling indicate biogenic signatures within the light-end hydrocarbon fractions; however, this isotopic signature was not present in the headspace gas of low-temperature hydrous pyrolysis (LTHP) experiments. These observations raise questions regarding the maturation pathway and associated fluid evolution for this source rock reservoir and whether apparent in-situ fluid volatility may enhance the exploitation of this resource in lower thermal maturity windows. This research work provides unique opportunities to advance the fundamental understanding of hydrocarbon generation and production in calcareous organic‐rich shales from a prospective Australian Basin, with potential implications for other similar organic‐rich shale plays globally.
- Oceania > Australia > South Australia (1.00)
- Oceania > Australia > Queensland (1.00)
- Oceania > Australia > New South Wales (1.00)
- Phanerozoic > Mesozoic > Cretaceous > Upper Cretaceous (0.46)
- Phanerozoic > Mesozoic > Cretaceous > Lower Cretaceous > Albian (0.46)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Petroleum Play Type > Unconventional Play > Shale Play (1.00)
- Geology > Geological Subdiscipline > Geochemistry (1.00)
- Geology > Geological Subdiscipline > Economic Geology > Petroleum Geology (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- Materials > Chemicals > Commodity Chemicals > Petrochemicals (0.47)
- Oceania > New Zealand > North Island > Taranaki Basin (0.99)
- Oceania > Australia > South Australia > Warburton Basin (0.99)
- Oceania > Australia > South Australia > Great Artesian Basin (0.99)
- (54 more...)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale oil (1.00)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale gas (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- (3 more...)
Summary Long-term (multiyear) buildup tests conducted for multifractured horizontal wells (MFHWs) completed in shale reservoirs offer the unique opportunity to study and analyze flow-regimes sequences that are not commonly observed with typical buildup test periods. In this study, two buildup periods (including a rarely observed, nearly 5-year buildup), and the preceding extended flow tests, were analyzed for an MFHW completed in an Australian shale gas reservoir within the Beetaloo Basin. The objectives of the analyses were to (a) identify the sequence of flow regimes observed for each test (flow/buildup, F/BU) period; (b) extract estimates of reservoir permeability and hydraulic fracture properties; and (c) study the evolution of these properties with each subsequent test. An MFHW, the Amungee NW-1H, completed in the Velkerri B shale in Australia, was analyzed. Due to a casing deformation and inability to mill out plugs beyond this, most of the flow contribution was from the heel stages of the well. The first F/BU period was conducted from 2016 to 2021 (a nearly 5-year buildup), while the second F/BU was initiated in 2021 (buildup is currently continuing). The extended (>1 month) production tests (EPTs) preceding the buildups were analyzed using rate-transient analysis (RTA) methods [flow-regime identification/straightline /type curve analysis (TCA)] modified for shale gas properties (e.g., desorption), while the buildups were analyzed using classic pressure-transient analysis (PTA) methods. The first (~5-year) buildup period (BU 1) revealed a sequence of bilinear-linear-elliptical-pseudoradial flow followed by a second linear flow period. The first two flow regimes are interpreted to be associated with interfracture flow, while the latter is assumed to correspond to linear flow to the well. Elliptical/radial flow around fractures is rationalized to occur due to interpreted relatively short fracture half-lengths (corresponding to the high-conductivity portion of the fractures). Permeability estimates are in good agreement with diagnostic fracture injection test (DFIT) analysis. Flow-regime interpretations for the other test periods (EPTs 1 and 2, BU 2) are largely consistent, although EPT 1 flow-regime interpretation was challenged by noisy data. Permeability values derived from EPTs 1 and 2 are smaller than from buildup tests, suggesting stress sensitivity caused by drawdown. Properties derived from the analysis of BU 1 and 2 are in good agreement, suggesting that any effects caused by stress sensitivity of reservoir parameters are largely reversible. Permeability derived from all tests are much larger than those obtained from laboratory data, leading to the interpretation that natural fractures are elevating system permeability. Fracture half-lengths are also much shorter than those typically reported for MFHWs. The mostly “textbook” quality well test data obtained for this field example, combined with the length of the test periods, resulted in one of the most complete flow-regime sequences observed for an MFHW completed in a shale gas reservoir. The existence of a radial flow period observed for all test periods (interpreted to be interfracture radial flow) allows for confident estimates of reservoir permeability/skin and their evolution with each subsequent test, which is rarely reported. The radial-flow-derived permeability, combined with early linear flow analysis, also allowed fracture half-length to be estimated for all tests. This case study adds significantly to our understanding of shale gas reservoir characteristics and flow-regime sequences associated with MFHWs.
- North America > Canada (0.94)
- North America > United States > Texas (0.94)
- Oceania > Australia > Northern Territory (0.66)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geophysics > Borehole Geophysics (0.68)
- Geophysics > Seismic Surveying (0.46)
- Oceania > Australia > Northern Territory > Beetaloo Basin > Beetaloo Extension Basin > EP 98 > Kalata South Field > Kalata South 1 Well (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- North America > United States > South Dakota > Williston Basin > Bakken Shale Formation (0.99)
- (5 more...)
Fault Identification for the Purposes of Evaluating the Risk of Induced Seismicity: A Novel Application of the Flowback DFIT (DFIT-FBA)
Zeinabady, Danial (University of Calgary) | Clarkson, Christopher R. (University of Calgary) | Razzaghi, Samaneh (Ovintiv Inc.) | Haqparast, Sadjad (University of Calgary) | Benson, Abdul-Latif L. (University of Calgary) | Azad, Mohammad (University of Calgary)
Abstract The existence of faults, pre-existing hydraulic fractures, and depleted areas can have negative impacts on the development of unconventional reservoirs using multi-fractured horizontal wells (MFHWs). For example, the triggering of fault slippage through hydraulic fracturing can create the environmental hazard known as induced seismicity (earthquakes caused by hydraulic fracturing). A premium has therefore been placed on the development of technologies that can be used to identify the locations of fault systems (particularly if they are subseismic), as well as pre-existing hydraulic fractures and depleted areas that can similarly negatively impact reservoir exploitation. The objective of this study is to develop a diagnostic tool to identify these conditions using DFIT-FBA. DFIT-FBA is a modified diagnostic fracture injection test (DFIT) whereby a sequence of injection and flowback steps are performed to estimate minimum in-situ stress, fracture surface area, reservoir pressure, and permeability in shale and tight reservoirs. The time- and cost-efficiency of the DFIT-FBA method provides an opportunity to conduct multiple field tests at a single point, or along the lateral section of a horizontal well, without significantly delaying the completion program. The proposed diagnostic tool uses an analytical model which considers critical processes and mechanisms occurring during a DFIT-FBA test, including wellbore storage, leakoff rate, and fracture stiffness development. The results of analytical modeling demonstrate that faults, pre-existing hydraulic fractures, and depleted areas of the reservoir can be identified either by implementing multiple cycles of the DFIT-FBA test at a single point, or by applying multiple DFIT-FBA tests at different points along the lateral section of a horizontal well or at different wells. The analytical model is first verified using a fully-coupled hydraulic fracture, reservoir, and wellbore simulator, and flowing pressure responses in the presence of different reservoir heterogeneities are then illustrated. Practical application of the proposed method is demonstrated using DFIT-FBA field examples performed in a tight reservoir. Analysis of the field examples results in the conclusion that a fault occurs near the toe of the horizontal lateral. This finding was confirmed by other field information and provides the opportunity to modify the main-stage hydraulic fracturing design to avoid induced seismicity events. This study proposes a novel, fast, and low-cost approach for identifying faults, pre-existing hydraulic fractures, and depleted areas using the DFIT-FBA test. The recommended approach can help engineers to characterize the reservoir quality along a horizontal well, as well as identify features/conditions that could negatively influence reservoir development, such as faults (and the possibility of creating induced seismicity), pre-existing hydraulic fractures, and reservoir depletion.
- North America > Canada > British Columbia (1.00)
- North America > Canada > Alberta (1.00)
- North America > United States > Texas (0.69)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Structural Geology > Tectonics > Plate Tectonics > Earthquake (0.88)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.35)
- North America > Canada > Saskatchewan > Western Canada Sedimentary Basin > Alberta Basin (0.99)
- North America > Canada > Northwest Territories > Western Canada Sedimentary Basin > Alberta Basin (0.99)
- North America > Canada > Manitoba > Western Canada Sedimentary Basin > Alberta Basin (0.99)
- (9 more...)
- Well Completion > Hydraulic Fracturing (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Reservoir geomechanics (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Pressure transient analysis (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Drillstem/well testing (1.00)
Generating a labeled data set to train machine learning algorithms for lithologic classification of drill cuttings
Becerra, Daniela (University of Calgary) | Pires de Lima, Rafael (Geological Survey of Brazil) | Galvis-Portilla, Henry (University of Calgary) | Clarkson, Christopher R. (University of Calgary)
Abstract Despite significant developments in the past few years in the application of machine learning algorithms for the lithologic classification of rock samples, publicly available labeled data sets are very scarce. We open source a fully labeled data set containing more than 16,000 scanning electron microscopy (SEM) images of drill cutting samples—mounted on thin sections—from a low-permeability reservoir in western Canada. We develop a simplified image processing workflow to segment and isolate the rock chips into individual SEM images, which in turn are used to identify, classify, and quantify rock types based on textural characteristics. In addition, using this data set, we explore the use of convolutional neural networks (CNNs) as a baseline tool for acceleration and automatization of rock-type classification. Without significant modifications to popular CNN models, we obtain an accuracy of approximately 90% for the test set. Results demonstrate the potential of CNN as a fast approach for lithologic classification in low-permeability siltstone reservoirs. In addition to making the data set publicly available, we believe our workflow to segment and isolate drill cutting samples in individual images of rock chips will facilitate future research of drill cuttings properties (e.g., lithology, porosity, and particle size) using machine learning algorithms.
- Research Report > New Finding (0.48)
- Research Report > Experimental Study (0.48)
- Oceania > Australia > South Australia > Cooper Basin (0.99)
- Oceania > Australia > Queensland > Cooper Basin (0.99)
- North America > United States > South Dakota > Williston Basin > Bakken Shale Formation (0.99)
- (30 more...)
Optimizing the Huff ‘n’ Puff Gas Injection Performance in Shale Reservoirs Considering the Uncertainty: A Duvernay Shale Example
Hamdi, Hamidreza (University of Calgary) | Clarkson, Christopher R. (University of Calgary) | Esmail, Ali (Ovintiv Corporation) | Sousa, Mario Costa (University of Calgary)
Summary Recent studies have indicated that huff ‘n’ puff (HNP) gas injection has the potential to recover an additional 30 to 70% oil from multifractured horizontal wells in shale reservoirs. Nonetheless, this technique is very sensitive to production constraints and is impacted by uncertainty related to measurement quality (particularly frequency and resolution) and lack of constraining data. In this paper, a Bayesian workflow is provided to optimize the HNP process under uncertainty using a Duvernay shale well as an example. Compositional simulations are conducted that incorporate a tuned pressure/volume/temperature (PVT) model and a set of measured cyclic injection/compaction pressure‐sensitive permeability data. Markov‐Chain Monte Carlo (MCMC) is used to estimate the posterior distributions of the model uncertain variables by matching the primary production data. The MCMC process is accelerated by using an accurate proxy model (kriging) that is updated using a highly adaptive sampling algorithm. Gaussian processes are then used to optimize the HNP control variables by maximizing the lower confidence interval (μ‐σ) of cumulative oil production (after 10 years) across a fixed ensemble of uncertain variables sampled from posterior distributions. The uncertain variable space includes several parameters representing reservoir and fracture properties. The posterior distributions for some parameters, such as primary fracture permeability and effective half‐length, are narrower, whereas wider distributions are obtained for other parameters. The results indicate that the impact of uncertain variables on HNP performance is nonlinear. Some uncertain variables (such as molecular diffusion) that do not show strong sensitivity during the primary production strongly impact gas injection HNP performance. The results of optimization under uncertainty confirm that the lower confidence interval of cumulative oil production can be maximized by an injection time of approximately 1.5 months, a production time of approximately 2.5 months, and very short soaking times. In addition, a maximum injection rate and a flowing bottomhole pressure around the bubblepoint are required to ensure maximum incremental recovery. Analysis of the objective function surface highlights some other sets of production constraints with competitive results. Finally, the optimal set of production constraints, in combination with an ensemble of uncertain variables, results in a median HNP cumulative oil production that is 30% greater than that for primary production. The application of a Bayesian framework for optimizing the HNP performance in a real shale reservoir is introduced for the first time. This work provides practical guidelines for the efficient application of advanced techniques for optimization under uncertainty, resulting in better decision making.
- North America > United States > Texas (1.00)
- North America > Canada > Alberta (1.00)
- Asia (1.00)
- (3 more...)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Petroleum Play Type > Unconventional Play > Shale Play (0.84)
- South America > Argentina > Patagonia > Neuquén > Neuquen Basin > Vaca Muerta Shale Formation (0.99)
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Eagle Ford Shale Formation (0.99)
- North America > United States > Texas > Sabinas - Rio Grande Basin > Eagle Ford Shale Formation (0.99)
- (19 more...)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (1.00)
Effects of Entrained Hydrocarbon and Organic-Matter Components on Reservoir Quality of Organic-Rich Shales: Implications for “Sweet Spot” Identification and Enhanced-Oil-Recovery Applications in the Duvernay Formation (Canada)
Ghanizadeh, Amin (University of Calgary) | Clarkson, Christopher R. (University of Calgary) | Clarke, Katherine M. (University of Calgary) | Yang, Zhengru (University of Calgary) | Rashidi, Behrad (University of Calgary) | Vahedian, Atena (University of Calgary) | Song, Chengyao (University of Calgary) | DeBuhr, Chris (University of Calgary) | Haghshenas, Behjat (University of Calgary) | Ardakani, Omid H. (Geological Survey of Canada) | Sanei, Hamed (Aarhus University) | Royer, Dean P. (Encana Corporation)
Summary The hydrocarbon (HC)‐storage capacity of organic‐rich shales depends on porosity and surface area, whereas pore‐throat‐size distribution and pore‐throat‐network connectivity control permeability. The pores within the organic matter (OM) of organic‐rich shales develop during thermal maturation as different HC phases are generated and expelled from the OM. Organic‐rich shales can potentially retain a large proportion of the HCs generated during the diagenesis process. Commercial HC production from liquid‐rich shale reservoirs can be achieved using completion technologies such as multistage‐fractured horizontal wells. However, the ability of industry to identify “sweet spots” along multistage‐fractured horizontal wells for both primary and enhanced oil recovery (EOR) is still hampered by insufficient understanding of the effects of type/content of entrained HC/OM components on reservoir quality. The primary objectives of the current study are therefore to establish an integrated experimental workflow to investigate the effect of entrained HC/OM on storage and transport properties of the organic‐rich shales, and to provide examples of that experimental workflow through analyzing a selected sample suite from a prolific shale‐oil reservoir (the Duvernay Formation) in western Canada. To accomplish this goal, a comprehensive suite of petrophysical analyses is performed on a diverse sample suite from the Duvernay Formation that differs in OM content (2.8 to 5 wt%; n = 5), before and after sequential pyrolysis by a revised Rock‐Eval analysis [extended‐slow‐heating (ESH) Rock‐Eval analysis]. Using the ESH cycle, different HC/OM components can be distinguished more easily and reliably during the pyrolysis process: free light oil (S1ESH; up to 150°C), fluid‐like HC residue (FHR) (S2a; 150 to 380°C), and solid bitumen/residual carbon (S2b; 380 to 650°C). The characterization techniques used at each stage are helium pycnometry (grain density, helium porosity); low‐pressure gas [nitrogen (N2), carbon dioxide (CO2)] adsorption (LPA) [pore volume (PV), surface area, pore-size distribution (PSD) within micropores, mesopores, and smaller macropores]; crushed‐rock gas [helium, CO2, N2] permeability; and rate‐of‐adsorption (ROA) analysis (CO2, N2). Scanning‐electron‐microscopy (SEM) analysis is further conducted to verify/support the petrophysical observations. Powder X‐ray‐diffraction (XRD) analyses were performed on all samples in the “as‐received” state and after Stage S2b (thermal pyrolysis up to 650°C) to quantify variations in mineralogical compositions and their possible controls on the evolution of petrophysical properties (i.e., porosity/permeability). Organic petrography was conducted on selected samples to characterize the nature of OM. Compared with the “as‐received” state, porosity, permeability, modal‐pore‐size distribution, and surface‐area increase with sequential pyrolysis stages, associated with the expulsion and devolatilization of free light oil and FHR (S2a; up to 380°C). However, the change in petrophysical properties associated with the degradation of solid bitumen/residual carbon (S2b; up to 650°C) is variable and unpredictable. The observed reduction in porosity/permeability values after Stage S2b is likely attributed to the occlusion of PV with solid bitumen/residual carbon degradation (i.e., coking); sample swelling caused by water loss from the lattice structure of clay minerals (i.e., illite); and sample compaction as a result of OM removal from the rock matrix. Among various stages of the ESH Rock‐Eval pyrolysis, the petrophysical properties that are measured after Stages S1ESH and S2a, as they are related to the expulsion of the lighter and heavier free‐HC compounds from the rock matrix, are expected to be the most important for primary and EOR applications. Quantification of the evolution of reservoir quality with HC generation/expulsion has important implications for identifying petrophysical “sweet spots” within unconventional reservoirs, optimizing stimulation design, and targeting specific zones within the reservoir of interest with the OM content/type amenable to maximizing gas storage/transport during cyclic solvent injection for EOR applications. The integrated experimental workflow proposed herein could be of significant interest to the operators of organic‐rich shale/mudstone plays (e.g., the Duvernay) as a screening tool for developing optimized stimulation treatments for improving primary and enhanced HC recovery.
- Research Report > New Finding (1.00)
- Research Report > Experimental Study (1.00)
- Personal (0.67)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Geological Subdiscipline > Geochemistry (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- Materials > Chemicals > Commodity Chemicals > Petrochemicals (0.66)
- North America > United States > Wyoming > Uinta Basin (0.99)
- North America > United States > West Virginia > Appalachian Basin > Marcellus Shale Formation (0.99)
- North America > United States > Virginia > Appalachian Basin > Marcellus Shale Formation (0.99)
- (32 more...)
A Bayesian Approach for Optimizing the Huff-n-Puff Gas Injection Performance in Shale Reservoirs Under Parametric Uncertainty: A Duvernay Shale Example
Hamdi, Hamidreza (University of Calgary) | Clarkson, Christopher R. (University of Calgary) | Esmail, Ali (Encana Corporation) | Costa Sousa, Mario (University of Calgary)
Abstract Recent studies have indicated that Huff-n-Puff (HNP) gas injection has the potential to recover an additional 30-70% oil from multi-fractured horizontal wells in shale reservoirs. Nonetheless, this technique is very sensitive to production constraints and is impacted by uncertainty related to measurement quality (particularly frequency and resolution), and lack of constraining data. In this paper, a Bayesian workflow is provided to optimize the HNP process under uncertainty using a Duvernay shale well as an example. Compositional simulations are conducted which incorporate a tuned PVT model and a set of measured cyclic injection/compaction pressure-sensitive permeability data. Markov chain Monte Carlo (McMC) is used to estimate the posterior distributions of the model uncertain variables by matching the primary production data. The McMC process is accelerated by employing an accurate proxy model (kriging) which is updated using a highly adaptive sampling algorithm. Gaussian Processes are then used to optimize the HNP control variables by maximizing the lower confidence interval (μ-σ) of cumulative oil production (after 10 years) across a fixed ensemble of uncertain variables sampled from posterior distributions. The uncertain variable space includes several parameters representing reservoir and fracture properties. The posterior distributions for some parameters, such as primary fracture permeability and effective half-length, are narrower, while wider distributions are obtained for other parameters. The results indicate that the impact of uncertain variables on HNP performance is nonlinear. Some uncertain variables (such as molecular diffusion) that do not show strong sensitivity during the primary production strongly impact gas injection HNP performance. The results of optimization under uncertainty confirm that the lower confidence interval of cumulative oil production can be maximized by an injection time of around 1.5 months, a production time of around 2.5 months, and very short soaking times. In addition, a maximum injection rate and a flowing bottomhole pressure around the bubble point are required to ensure maximum incremental recovery. Analysis of the objective function surface highlights some other sets of production constraints with competitive results. Finally, the optimal set of production constraints, in combination with an ensemble of uncertain variables, results in a median HNP cumulative oil production that is 30% greater than that for primary production. The application of a Bayesian framework for optimizing the HNP performance in a real shale reservoir is introduced for the first time. This work provides practical guidelines for the efficient application of advanced machine learning techniques for optimization under uncertainty, resulting in better decision making.
- North America > Canada > Alberta (1.00)
- Europe (1.00)
- North America > United States > Texas (0.68)
- (2 more...)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Petroleum Play Type > Unconventional Play (0.84)
- North America > United States > Texas > Permian Basin > Delaware Basin (0.99)
- North America > United States > South Dakota > Williston Basin > Bakken Shale Formation (0.99)
- North America > United States > North Dakota > Williston Basin > Bakken Shale Formation > Middle Bakken Shale Formation (0.99)
- (12 more...)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (1.00)