Artificial Intelligence (AI) Assisted History Matching

Shahkarami, Alireza; Mohaghegh, Shahab D.; Gholami, Vahid; Haghighat, S. Alireza

doi:10.2118/169507-ms

Cited by 47 publications

(25 citation statements)

References 69 publications

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“…During the last few years, AI techniques including artificial neural network, fuzzy logic, support vector machine, genetic algorithms, adaptive neuro-fuzzy inference system and swarm intelligence became increasingly popular in the petroleum industry. AI techniques are applied in different aspects of petroleum engineering such as production monitoring, forecasting and multilateral well evaluation (Velazquez et al 2012;Weiss et al 2002), PVT parameter prediction (Weiss et al 2002;Khaksar et al 2016;Alarfaj et al 2012;Elkatatny and Mahmoud 2018), well integrity evaluation (Al-Ajmi et al 2015), assisted history matching (Al-Thuwaini et al 2006;Shahkarami et al 2014), interpreting well logging data and well to well correlation (Saggaf and Nebrija 1998;Wu and Nyland 1986;Lim et al 1998a, b;Wiener et al 1995;Denney 1998)…”

Section: Artificial Intelligencementioning

confidence: 99%

New approach to evaluate the equivalent circulating density (ECD) using artificial intelligence techniques

Abdelgawad

Elzenary

Elkatatny

et al. 2018

J Petrol Explor Prod Technol

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The equivalent circulation density (ECD) is a very important parameter in drilling high-pressure high-temperature and deepwater wells. ECD is a key parameter during drilling through formations where the margin between the pore pressure and the fracture pressure (FP) is narrow. In these critical formations, the ECD is used to control the formation pressure and prevent kicks. Recent approaches in oilfields to determine ECD depend mainly on using expensive downhole sensors for providing real-time values of ECD. Most of these tools have operational limitations such as high pressure and high temperature which may prevent using these tools in downhole conditions. The objective of this paper is to develop a new approach for predicting ECD using artificial intelligence (AI) techniques from surface drilling parameters [mud weight, drill pipe pressure, and rate of penetration (ROP)]. 2376 data points were used to develop the AI models. The data were collected during the drilling of an 8.5″ vertical hole section. Two AI models were used to estimate the ECD: artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). An empirical correlation for ECD was derived from the optimized ANN model by extracting the weights and biases. The developed ANN and ANFIS models were able to calculate ECD with a correlation coefficient (R) of 0.99 and average absolute percentage error of 0.22% for ANN and ANFIS models, respectively. The developed empirical correlation for the ANN model can be used during well design to choose a correct mud weight to safely drill the well based on the expected drilling parameters. It will also minimize the drilling problems related to ECD such as losses or gains especially in critical situations where the margin between the pore and fracture pressure is very narrow. In addition, using this technique will save cost and time by reducing the need for expensive, complicated downhole tools.

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Section: Artificial Intelligencementioning

confidence: 99%

New approach to evaluate the equivalent circulating density (ECD) using artificial intelligence techniques

Abdelgawad

Elzenary

Elkatatny

et al. 2018

J Petrol Explor Prod Technol

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“…Recently, the data-driven-based AHM models have been proposed with the rapid development of machine learning techniques [18][19][20]. The past decade has witnessed various proxy models for AHM based on machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching

Zhang

Jiang

et al. 2020

Energies

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Reservoir history matching is a well-known inverse problem for production prediction where enormous uncertain reservoir parameters of a reservoir numerical model are optimized by minimizing the misfit between the simulated and history production data. Gaussian Process (GP) has shown promising performance for assisted history matching due to the efficient nonparametric and nonlinear model with few model parameters to be tuned automatically. Recently introduced Gaussian Processes proxy models and Variogram Analysis of Response Surface-based sensitivity analysis (GP-VARS) uses forward and inverse Gaussian Processes (GP) based proxy models with the VARS-based sensitivity analysis to optimize the high-dimensional reservoir parameters. However, the inverse GP solution (GPIS) in GP-VARS are unsatisfactory especially for enormous reservoir parameters where the mapping from low-dimensional misfits to high-dimensional uncertain reservoir parameters could be poorly modeled by GP. To improve the performance of GP-VARS, in this paper we propose the Gaussian Processes proxy models with Latent Variable Models and VARS-based sensitivity analysis (GPLVM-VARS) where Gaussian Processes Latent Variable Model (GPLVM)-based inverse solution (GPLVMIS) instead of GP-based GPIS is provided with the inputs and outputs of GPIS reversed. The experimental results demonstrate the effectiveness of the proposed GPLVM-VARS in terms of accuracy and complexity. The source code of the proposed GPLVM-VARS is available at https://github.com/XinweiJiang/GPLVM-VARS.

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“…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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Artificial Intelligence (AI) Assisted History Matching

Cited by 47 publications

References 69 publications

New approach to evaluate the equivalent circulating density (ECD) using artificial intelligence techniques

New approach to evaluate the equivalent circulating density (ECD) using artificial intelligence techniques

Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching

Smart Proxy Modeling of SACROC CO2-EOR

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