Reservoir simulation and modeling based on artificial intelligence and data mining (AI&amp;DM)

Mohaghegh, Shahab D.

doi:10.1016/j.jngse.2011.08.003

Cited by 78 publications

(25 citation statements)

References 12 publications

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“…The nature and class of the AI-based shale reservoir model is determined by the source of this database. The term spatiotemporal defines the essence of this database and is inspired from the physics that controls this phenomenon (Mohaghegh, 2011). An extensive data mining and analysis process should be conducted at this step to fully understand the data that is housed in this database.…”

Section: Top-down Modeling E Pattern Recognition Based Reservoir Modementioning

confidence: 99%

“…Both training and calibration datasets that are used during the initial training and history matching of the model are considered non-blind. As noted by Mohaghegh (2011), some may argue that the calibration -also known as testing dataset -is also blind. This argument has some merits but if used during the development of the AI-based shale reservoir model can compromise validity and predictability of the model and therefore such practices are not recommended.…”

Section: Top-down Modeling E Pattern Recognition Based Reservoir Modementioning

confidence: 99%

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Full field reservoir modeling of shale assets using advanced data-driven analytics

Esmaili

Mohaghegh

2016

Geoscience Frontiers

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a b s t r a c tHydrocarbon production from shale has attracted much attention in the recent years. When applied to this prolific and hydrocarbon rich resource plays, our understanding of the complexities of the flow mechanism (sorption process and flow behavior in complex fracture systems -induced or natural) leaves much to be desired. In this paper, we present and discuss a novel approach to modeling, history matching of hydrocarbon production from a Marcellus shale asset in southwestern Pennsylvania using advanced data mining, pattern recognition and machine learning technologies. In this new approach instead of imposing our understanding of the flow mechanism, the impact of multi-stage hydraulic fractures, and the production process on the reservoir model, we allow the production history, well log, completion and hydraulic fracturing data to guide our model and determine its behavior. The uniqueness of this technology is that it incorporates the so-called "hard data" directly into the reservoir model, so that the model can be used to optimize the hydraulic fracture process. The "hard data" refers to field measurements during the hydraulic fracturing process such as fluid and proppant type and amount, injection pressure and rate as well as proppant concentration. This novel approach contrasts with the current industry focus on the use of "soft data" (non-measured, interpretive data such as frac length, width, height and conductivity) in the reservoir models. The study focuses on a Marcellus shale asset that includes 135 wells with multiple pads, different landing targets, well length and reservoir properties. The full field history matching process was successfully completed using this data driven approach thus capturing the production behavior with acceptable accuracy for individual wells and for the entire asset.Ó 2015, China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

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Section: Top-down Modeling E Pattern Recognition Based Reservoir Modementioning

confidence: 99%

Section: Top-down Modeling E Pattern Recognition Based Reservoir Modementioning

confidence: 99%

Full field reservoir modeling of shale assets using advanced data-driven analytics

Esmaili

Mohaghegh

2016

Geoscience Frontiers

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“…Mohaghegh describes SRM as an "ensemble of multiple, interconnected neuro-fuzzy systems that are trained to adaptively learn the fluid flow behavior from a multi-well, multilayer reservoir simulation model, such that they can reproduce results similar to those of the reservoir simulation model (with high accuracy) in real-time" [15]. Since 2006, SRM as a rapid replica of a numerical simulation model with quite high accuracy has been applied and validated in different case studies [16][17][18][19][20][21][22]. SRM can be categorized in well-based [17][18][19]21,23] or grid-based types [16,20,24] depending on the objective or the output of the model.…”

Section: Introductionmentioning

confidence: 99%

“…Since 2006, SRM as a rapid replica of a numerical simulation model with quite high accuracy has been applied and validated in different case studies [16][17][18][19][20][21][22]. SRM can be categorized in well-based [17][18][19]21,23] or grid-based types [16,20,24] depending on the objective or the output of the model. In a well-based SRM, the objective is to mimic the reservoir response at the well location in terms of production (or injection).…”

Section: Introductionmentioning

confidence: 99%

Smart Proxy Modeling of SACROC CO2-EOR

Gholami

Mohaghegh

Maysami

2019

Fluids

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Large CO 2 -enhanced oil recovery (EOR) projects usually contain an abundance of geological and good performance data. While this volume of data leads to robust models, it often results in difficult to manage, slow-running numerical flow models. To dramatically reduce the numerical run-times associated with the traditional simulation techniques, this work investigated the feasibility of using artificial intelligence and machine learning technologies to develop a smart proxy model of the Scurry Area Canyon Reef Operators Committee (SACROC) oilfield, located in the Permian Basin, TX, USA. Smart proxy models can be used to facilitate injection-production optimization for CO 2 -EOR projects. The use of a coupled grid-based, and well-based surrogate reservoir model (SRM) (also known as smart proxy modeling) was investigated as the base of the optimization. A fit-for-purpose coupled SRM, which executes in seconds, was built based on high-resolution numerical reservoir simulation models of the northern platform of the SACROC oilfield. This study is unique as it is the first application of coupled SRM at a large oilfield. The developed SRM was able to identify the dynamic reservoir properties (pressure, saturations, and component mole-fraction) at every grid-block, along with the production characteristics (pressure and rate) at each well. Recent attempts to use machine learning and pattern recognition to build proxy models have been simplistic, with limited predictive capabilities. The geological model used in this study is comprised of more than nine million grid blocks. The high correlation between the actual component and SRM, which can be visualized by mapping the properties, along with the fast footprint of the developed model demonstrate the successful application of this methodology.

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“…Coupled with the AI models, the advances of information and computer sciences enable the subsurface engineers to process and analyze the real-time field data more effectively. For example, Mohaghegh highlighted the capability of the intelligent model to analyze the real-time field production data and calibrate the reservoir model to make more accurate forecasting results [75]. He also pointed out the importance of AI system by analyzing the real-time pressure and production data for the purpose of monitoring the well performance and diagnosing the inter-well connectivity [76].…”

mentioning

confidence: 99%

Artificial Intelligence Applications in Reservoir Engineering: A Status Check

Ertekin

Sun

2019

Energies

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This article provides a comprehensive review of the state-of-art in the area of artificial intelligence applications to solve reservoir engineering problems. Research works including proxy model development, artificial-intelligence-assisted history-matching, project design, and optimization, etc. are presented to demonstrate the robustness of the intelligence systems. The successes of the developments prove the advantages of the AI approaches in terms of high computational efficacy and strong learning capabilities. Thus, the implementation of intelligence models enables reservoir engineers to accomplish many challenging and time-intensive works more effectively. However, it is not yet astute to completely replace the conventional reservoir engineering models with intelligent systems, since the defects of the technology cannot be ignored. The trend of research and industrial practices of reservoir engineering area would be establishing a hand-shaking protocol between the conventional modeling and the intelligent systems. Taking advantages of both methods, more robust solutions could be obtained with significantly less computational overheads.

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Reservoir simulation and modeling based on artificial intelligence and data mining (AI&DM)

Cited by 78 publications

References 12 publications

Full field reservoir modeling of shale assets using advanced data-driven analytics

Full field reservoir modeling of shale assets using advanced data-driven analytics

Smart Proxy Modeling of SACROC CO2-EOR

Artificial Intelligence Applications in Reservoir Engineering: A Status Check

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