Converting detail reservoir simulation models into effective reservoir management tools using SRMs; case study - three green fields in Saudi Arabia

Mohaghegh, Shahab D.

doi:10.1504/ijogct.2014.059284

Cited by 12 publications

(8 citation statements)

References 8 publications

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“…SRMs are relatively new tools for fast track and comprehensive reservoir analysis, which originate from the existing reservoir simulation models. In other words, SRMs are approximations of the full field three dimensional numerical reservoir models and are capable of accurately mimicking the behavior of these full field models (Mohaghegh, 2014). In this study, SRMs are built based on artificial neural networks.…”

Section: Surrogate Reservoir Modelsmentioning

confidence: 99%

“…Mohaghegh et al (2012a;2012b) have discussed the results of several projects involving surrogate reservoir models for the fast track analysis of numerical simulation models. Other publications regarding the SRMs can be found in variety of reference materials (Mohaghegh, 2009;Mohaghegh, 2011;Mohaghegh, 2014;Shahkarami, et al, 2014a;Amini, et al, 2014).…”

Section: Surrogate Reservoir Modelsmentioning

confidence: 99%

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Assisted History Matching Using Pattern Recognition Technology

Shahkarami¹,

Mohaghegh

Hajizadeh

2015

SPE Digital Energy Conference and Exhibition

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This study aims to examine the application of pattern recognition technologies to improve the time and efforts required for completing a successful history matching project. The pattern recognition capabilities of artificial intelligence and data mining techniques are used to develop a Surrogate Reservoir Model (SRM) and use it to perform the assisted history matching process. A well-known standard reservoir model, PUNQ-S3, was selected to examine the potentials of SRM in an assisted history matching process.SRM is a prototype of full field reservoir simulation model that runs in a matter of seconds. SRMs are built based on a spatiotemporal database. The database includes different types of data extracted from a few realizations of the simulation model. In this study, the SRM was developed using ten geological realizations of PUNQ-S3 reservoir simulation model. The uncertain properties are distributions of porosity, horizontal, and vertical permeability. The SRM requires low development cost and has high implementation pace. The SRM was coupled with the Differential Evolution (DE) optimization method to construct an automated history matching workflow. This workflow is able to produce multiple realizations of the reservoir, which match the past performance.The developed SRM showed a high accuracy in mimicking the behavior of reservoir simulation model. Once we select the best performing cases during history matching, we were able to also obtain relaiable future forecasts for the model. The results of this study prove the cability of SRMs in assisting history matching process using population-based sampling algorithms and other computationally intensive operations in reservoir management workflows.

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Section: Surrogate Reservoir Modelsmentioning

confidence: 99%

Section: Surrogate Reservoir Modelsmentioning

confidence: 99%

Assisted History Matching Using Pattern Recognition Technology

Shahkarami¹,

Mohaghegh

Hajizadeh

2015

SPE Digital Energy Conference and Exhibition

Self Cite

View full text Add to dashboard Cite

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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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“…Previous efforts on surrogate modeling for reservoir development could be found in [14,16,[18][19][20]26]. These approaches were able to predict either well-bywell or a field scale production.…”

Section: Introduction and Justificationmentioning

confidence: 99%

Artificial Neural Network Surrogate Modeling of Oil Reservoir: A Case Study

Судаков

Koroteev

Belozerov

et al. 2019

Lecture Notes in Computer Science

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We develop a data-driven model, introducing recent advances in machine learning to reservoir simulation. We use a conventional reservoir modeling tool to generate training set and a special ensemble of artificial neural networks (ANNs) to build a predictive model. The ANNbased model allows to reproduce the time dependence of fluids and pressure distribution within the computational cells of the reservoir model. We compare the performance of the ANN-based model with conventional reservoir modeling and illustrate that ANN-based model (1) is able to capture all the output parameters of the conventional model with very high accuracy and (2) demonstrate much higher computational performance. We finally elaborate on further options for research and developments within the area of reservoir modeling.

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Converting detail reservoir simulation models into effective reservoir management tools using SRMs; case study - three green fields in Saudi Arabia

Cited by 12 publications

References 8 publications

Assisted History Matching Using Pattern Recognition Technology

Assisted History Matching Using Pattern Recognition Technology

Smart Proxy Modeling of SACROC CO2-EOR

Artificial Neural Network Surrogate Modeling of Oil Reservoir: A Case Study

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