Reservoir Simulation and Modeling Based on Pattern Recognition

Mohaghegh, Shahab D.

doi:10.2118/143179-ms

Cited by 39 publications

(19 citation statements)

References 11 publications

(8 reference statements)

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“…ANNs are non-linear data driven, fact and example based and most importantly a self-adaptive approach. These characteristics make them an ideal modeling tool for petroleum engineering problems (Haykin, 2008;Mohaghegh, 2011;Kriesel, 2011).…”

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

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“…54 Mohaghegh thoroughly discussed this step of SRM development in his paper. 64 In order to create the spatio-temporal database, the first step is to identify the number of runs that are required to develop the SRM. The purpose of having different realizations of a reservoir simulation model is to introduce the uncertainties involved in the model to the SRM.…”

Section: Srm Developmentmentioning

confidence: 99%

Application of Artificial Intelligence and Data Mining Techniques for Fast Track Modeling of Geologic Sequestration of Carbon Dioxide – Case Study of SACROC Unit

Shahkarami¹,

Mohaghegh

Gholami

et al. 2015

SPE Digital Energy Conference and Exhibition

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The complexities involved in the available reservoir simulation model for the geologic CO2 sequestration study at SACROC Unit, lead to a high computational cost nearly impractical for different types of reservoir studies. In this study, as an alternative to the full-field reservoir simulation model, we develop and examine the application of a new technology (Surrogate Reservoir Model -SRM) for fast track modeling of pressure and phase saturation distributions in the injection and post-injection time periods.The SRM is developed based on a few realizations of full-field reservoir simulation model, and it is able to generate the outputs in a very short time with reasonable accuracy. The SRM is developed using the pattern recognition capabilities of Artificial Intelligence and Data Mining (AI&DM) techniques. The SRM is trained based on the provided examples of the system and then verified using additional samples.The intricacy of simulating multiphase flow, having large number of time steps required to study injection and post-injection periods of CO2 sequestration, highly heterogeneous reservoir, and a large number of wells have led to a highly complicated reservoir simulation model for SACROC Unit. A single realization of this model takes hours to run. An in-depth understanding of CO2 sequestration process requires multiple realizations of the reservoir model. Consequently, using a conventional numerical simulator makes the computational cost of the analysis too high to be practical.On the other hand, the developed SRM for this case study runs in a matter of seconds. The comparison between the results of SRM and simulator, during training and verification steps of SRM development, demonstrates the ability of SRM in mimicking the behavior of numerical simulation model. The results of this study are intended to prove the potential of AI&DM based reservoir models, like SRM, to ease the obstacles involved in the conventional CO2 sequestration modeling.

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“…Classified as an AI-based Reservoir Model (Mohaghegh, 2011), the Surrogate Reservoir Model (SRM) is defined as an accurate replica of a reservoir simulation model that runs in real-time. Developed for the first time to replicate a mature field in the Middle East (Mohaghegh, 2006a, 2006b, 2006cand Mohaghegh, 2009), SRM can be applied to both mature and green fields.…”

Section: Surrogate Reservoir Models (Srms)mentioning

confidence: 99%

Application of Surrogate Reservoir Model (SRM) to an Onshore Green Field in Saudi Arabia; Case Study

Mohaghegh¹,

Liu

Gaskari

et al. 2012

All Days

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Application of the Surrogate Reservoir Model (SRM) to an onshore green field in Saudi Arabia is the subject of this paper. SRM is a recently introduced technology that is used to tap into the unrealized potential of the reservoir simulation models. High computational cost and long processing time of reservoir simulation models limit our ability to perform comprehensive sensitivity analysis, quantify uncertainties and risks associated with the geologic and operational parameters or to evaluate a large set of scenarios for development of green fields. SRM accurately replicates the results of a numerical simulation model with very low computational cost and low turnaround period and allows for extended study of reservoir behavior and potentials. SRM represents the application of artificial intelligence and data mining to reservoir simulation and modeling.In this paper, development and the results of the SRM for an onshore green field in Saudi Arabia is presented. A reservoir simulation model has been developed for this green field using Saudi Aramco's in-house POWERS™ simulator. The geological model that serves as the foundation of the simulation model is developed using an analogy that incorporates limited measured data augmented with information from similar fields producing from the same formations. The reservoir simulation model consists of 1.4 million active grid blocks, including 40 vertical production wells and 22 vertical water injection wells.Steps involved in developing the SRM are identifying the number of runs that are required for the development of the SRM, making the runs, extracting static and dynamic data from the simulation runs to develop the necessary spatio-temporal dataset, identifying the key performance indicators (KPIs) that rank the influence of different reservoir characteristics on the oil and gas production in the field, training and matching the results of the simulation model, and finally validating the performance of the SRM using a blind simulation run.SRM for this reservoir is then used to perform sensitivity analysis as well as quantification of uncertainties associated with the geological model. These analyses that require thousands of simulation runs were performed using the SRM in minutes.

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Reservoir Simulation and Modeling Based on Pattern Recognition

Cited by 39 publications

References 11 publications

Assisted History Matching Using Pattern Recognition Technology

Assisted History Matching Using Pattern Recognition Technology

Application of Artificial Intelligence and Data Mining Techniques for Fast Track Modeling of Geologic Sequestration of Carbon Dioxide – Case Study of SACROC Unit

Application of Surrogate Reservoir Model (SRM) to an Onshore Green Field in Saudi Arabia; Case Study

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