Practical implementation of knowledge-based approaches for steam-assisted gravity drainage production analysis

Ma, Zhiwei; Leung, Juliana Y.; Zanon, Stefan; Dzurman, Peter

doi:10.1016/j.eswa.2015.05.047

Cited by 45 publications

(3 citation statements)

References 62 publications

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“…An n-fold cross validation routine (Ma et al, 2015a) is implemented to select the optimal configuration based on the mean squared error (MSE) between the network outcomes and targets.…”

Section: Artificial Neural Network Modelingmentioning

confidence: 99%

“…ANN has been adopted in reservoir characterization, production forecast, history matching, production operation optimization and well design for many years (Al-Fattah and Startzman, 2011; An and Moon, 1993; Awoleke and Lane, 2011; Ayala H and Ertekin, 2007;Ramgulam, 2006;Stundner and Al-Thuwaini, 2001). ANN was also employed as a proxy model for SAGD production performance prediction in heterogeneous reservoirs by Ma et al (2015a;2015b). However, the application of ANN and other AI models in a history-matching framework for the characterization of shale barriers in SAGD reservoirs is rare.…”

Section: Introductionmentioning

confidence: 99%

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Integration of Artificial Intelligence and Production Data Analysis for Shale Heterogeneity Characterization in SAGD Reservoirs

Leung

Zanon

2016

Day 1 Tue, June 07, 2016

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SAGD recovery is strongly impacted by distributions of heterogeneous shale barriers, which impede the vertical growth and lateral spread of a steam chamber and potentially reduce oil production. Conventional reservoir heterogeneities characterization workflows that entail updating static reservoir models with dynamic flow data are quite time-consuming. Furthermore, numerical flow simulation could provide only approximate solutions to the recovery responses, as numerous simplifications and assumptions must be invoked. This study proposes a workflow integrating artificial intelligence (AI) in a model selection framework that aims to identify associated shale heterogeneities in SAGD reservoir based on observed decline patterns from production time-series data.A series of SAGD models based on typical Athabasca oil reservoir properties and operating conditions is constructed. Shale heterogeneities are modeled stochastically by sampling the location, lateral extent and thickness from probabilistic distributions inferred from field data. Each model is subjected to numerical flow simulation and the corresponding production profiles are recorded. First, sensitivity analysis is carried out to identify and parameterize particular patterns observable in the production response that are related to shale characteristics: whenever the steam chamber encounters a shale barrier, a drop in the production is observed; this drop continues until the steam chamber has advanced past the shale barrier, and the production would rise again. The positions and shapes of these decline patterns are retrieved. Next, artificial neural network (ANN) is constructed to calibrate a relationship between the retrieved production pattern parameters (inputs) and the corresponding geologic parameters describing shale heterogeneities (outputs), which include some variables capturing the location, size and orientation of a particular shale barrier encountered by the steam chamber.Shale barriers located close to the well pair have much more pronounced impacts on the production profiles. Shale barriers that are located far away do not exact a noticeable production pattern. The ANN models are validated using numerous synthetic reservoirs, where the exact shale distributions are known. The trained ANN models can reliably estimate the relevant shale parameters and the associated uncertainties, while accurately predicting the corresponding production responses. It is intended to extend the proposed method to construct the ANN models directly from well logs and production data.This work presents a preliminary attempt in correlating stochastic shale parameters with observable features in production time-series data using AI techniques. The proposed method facilitates the selection of an ensemble of reservoir models that are consistent with the production history; these models can be subjected to further history matching for a precise final match. The proposed methodology does not intend to replace traditional simulation and history-matching workflows, but it rathe...

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“…An n-fold cross validation routine (Ma et al, 2015a) is implemented to select the optimal configuration based on the mean squared error (MSE) between the network outcomes and targets.…”

Section: Artificial Neural Network Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integration of Artificial Intelligence and Production Data Analysis for Shale Heterogeneity Characterization in SAGD Reservoirs

Leung

Zanon

2016

Day 1 Tue, June 07, 2016

Self Cite

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“…To date, it has been successfully applied to various industries, including economics, computer science, financial trade, medical diagnosis, industry, transportation, and telematics [16][17][18]. Currently, AI technology has proven able to solve certain difficult problems in the oil industry [19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Prediction of drilling leakage locations based on optimized neural networks and the standard random forest method

Zhao

et al. 2021

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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Circulation loss is one of the most serious and complex hindrances for normal and safe drilling operations. Detecting the layer at which the circulation loss has occurred is important for formulating technical measures related to leakage prevention and plugging and reducing the wastage because of circulation loss as much as possible. Unfortunately, because of the lack of a general method for predicting the potential location of circulation loss during drilling, most current procedures depend on the plugging test. Therefore, the aim of this study was to use an Artificial Intelligence (AI)-based method to screen and process the historical data of 240 wells and 1029 original well loss cases in a localized area of southwestern China and to perform data mining. Using comparative analysis involving the Genetic Algorithm-Back Propagation (GA-BP) neural network and random forest optimization algorithms, we proposed an efficient real-time model for predicting leakage layer locations. For this purpose, data processing and correlation analysis were first performed using existing data to improve the effects of data mining. The well history data was then divided into training and testing sets in a 3:1 ratio. The parameter values of the BP were then corrected as per the network training error, resulting in the final output of a prediction value with a globally optimal solution. The standard random forest model is a particularly capable model that can deal with high-dimensional data without feature selection. To evaluate and confirm the generated model, the model is applied to eight oil wells in a well site in southwestern China. Empirical results demonstrate that the proposed method can satisfy the requirements of actual application to drilling and plugging operations and is able to accurately predict the locations of leakage layers.

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An AI-based workflow for estimating shale barrier configurations from SAGD production histories

Zheng

Leung

Sawatzky³

et al. 2018

Neural Comput & Applic

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Practical implementation of knowledge-based approaches for steam-assisted gravity drainage production analysis

Cited by 45 publications

References 62 publications

Integration of Artificial Intelligence and Production Data Analysis for Shale Heterogeneity Characterization in SAGD Reservoirs

Integration of Artificial Intelligence and Production Data Analysis for Shale Heterogeneity Characterization in SAGD Reservoirs

Prediction of drilling leakage locations based on optimized neural networks and the standard random forest method

An AI-based workflow for estimating shale barrier configurations from SAGD production histories

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