Designing cyclic pressure pulsing in naturally fractured reservoirs using an inverse looking recurrent neural network

Artun, Emre; Ertekin, Turgay; Watson, R.W.; Miller, B.D.

doi:10.1016/j.cageo.2011.05.006

Cited by 17 publications

(4 citation statements)

References 45 publications

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“…Several areas of application included reservoir characterization (Artun and Mohaghegh 2011;Raeesi et al 2012;Alizadeh et al 2012), candidate well selection for hydraulic fracturing treatments (Mohaghegh et al 1996), well-placement/trajectory optimization (Centilmen et al 1999;Doraisamy et al 2000;Johnson and Rogers 2001;Guyaguler and Horne 2000;Yeten et al 2003;Gokcesu et al 2005;Mohaghegh et al 2006), screening and optimization of secondary/enhanced oil recovery processes (Ayala and Ertekin 2005;Patel et al 2005;Demiryurek et al 2008;Artun et al 2010Artun et al , 2012Parada and Ertekin 2012;Amirian et al 2013), history matching (Cullick et al 2006Silva et al 2007;Zhao et al 2015), reservoir modeling, monitoring and management (Zangl et al 2006;Mohaghegh 2011;Mohaghegh et al 2014;Zhao et al 2015;Kalantari-Dhaghi et al 2015;Esmaili and Mohaghegh 2016). Most of these problems presented in the literature are based on development of artificial neural network (ANN) based proxy models that can accurately mimic reservoir models within a reasonable amount of accuracy and computational efficiency.…”

Section: Data-driven Modeling Approach Using Artificial Neural Networkmentioning

confidence: 99%

Characterizing Reservoir Connectivity and Forecasting Waterflood Performance Using Data-Driven and Reduced-Physics Models

Artun¹

2016

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Closed-loop reservoir management requires representative models that can be updated quickly. Such kind of models that can be used for characterization of reservoir connectivity would greatly contribute to decisions in waterflooding operations. In heterogeneous reservoirs that are characterized with thief-zones/ fractures or flow-barriers, that may enhance or prevent connectivity, it becomes challenging to construct geological models with both reasonable accuracy and computational efficiency. In this study, practical and computationally-efficient methods are presented to first accurately characterize the connectivity between producer and injector wells, and then to forecast the waterflood performance. Two classes of modeling approaches are evaluated in terms of their applicability to characterize the connectivity: 1) Data-driven modeling -artificial neural networks (ANN), in which there is not any presumed functional relationship and connectivity is derived from the weights on connection links of neural networks; 2) Reduced-physics modeling -capacitance-resistance models (CRM), in which physics are incorporated with certain assumptions and connectivity is derived from the fractional flow parameter in the formulation. In both approaches; a producer-based control volume is considered to focus on producers and their connectivity to different injectors, and both models are trained with injection/production histories. Connectivity values derived from these methods are fed into a short-term, pattern-based forecasting methodology, which quantified ultimate recoveries at the desired oil-cut values. Two synthetic reservoir models are used to test the applicability of aforementioned methods; which are characterized with high-permeability streaks and flow-barriers. These key features that affect connectivity are identified by both modeling approaches with reasonable accuracies, but with varying interpretations of fluid movement in the reservoir. Pattern-based forecasting methodology resulted in ultimate recovery predictions very close to the numerical-model based results, with errors within 3%. The workflow presented is quick and reliable to be implemented in closed-loop reservoir management protocols, which requires models that can be updated easily with recent data, and can be used in decision making processes with confidence.

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Section: Data-driven Modeling Approach Using Artificial Neural Networkmentioning

confidence: 99%

Characterizing Reservoir Connectivity and Forecasting Waterflood Performance Using Data-Driven and Reduced-Physics Models

Artun¹

2016

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“…A wide variety of neural network applications can be found in petroleum engineering (Mohaghegh 2002;Bravo et al 2012;Saputelli et al 2002;Stundner 2001), particularly in the areas of: classification (Stundner 2001), reservoir characterization or property prediction (Tang et al 2011;Raeesi et al 2012), proxy for recovery performance prediction (Awoleke & Lane 2011;Lechner & Zangl 2005), history matching (Ramagulam et al 2007), and design or optimization of production operations and well trajectory (Stoisits et al 1999;Luis et al 2007;Artun et al 2012;Yeten & Durlofsky 2003;Oberwinkler et al 2004;Malallah & Sami Nashawi 2005;Zangl et al 2006). In particular, neural networks have been utilized in recent years as a proxy model to predict heavy oil recoveries (Queipo et al 2002;Popa et al 2011;Popa & Patel 2012;Ahmadloo et al 2010;Aminian et al 2003); to perform EOR (enhanced oil recovery) screening (Zerafat et al 2011;Karambeigi et al 2011;Parada & Ertekin 2012); to characterize reservoir properties in unconventional plays (Holdaway 2012); and to evaluate performance of CO 2 sequestration process (Mohammadpoor et al 2012).…”

Section: Introductionmentioning

confidence: 99%

An Integrated Application of Cluster Analysis and Artificial Neural Networks for SAGD Recovery Performance Prediction in Heterogeneous Reservoirs

Amirian

Leung

Zanon

et al. 2014

Day 1 Tue, June 10, 2014

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Evaluation of steam-assisted gravity drainage (SAGD) performance that involves detailed compositional simulations is usually deterministic, cumbersome, expensive (manpower and time consuming), and not quite suitable for practical decision making and forecasting, particularly when dealing with highdimensional data space consisting of large number of operational and geological parameters. Data-driven modeling techniques, which entail comprehensive data analysis and implementation of machine learning methods for system forecast, provide an attractive alternative.In this paper, artificial neural network (ANN) is employed to predict SAGD production in heterogeneous reservoirs, an important application that is lacking in existing literature. Numerical flow simulations are performed to construct a training data set consists of various attributes describing characteristics associated with reservoir heterogeneities and other relevant operating parameters. Empirical Arps decline parameters are tested successfully for parameterization of cumulative production profile and considered as outputs of the ANN models. Sensitivity studies on network configurations are also investigated. Principal components analysis (PCA) is performed to reduce the dimensionality of the input vector, improve prediction quality, and limit over-fitting. In a case study, reservoirs with distinct heterogeneity distributions are fed to the model. It is shown that robustness and accuracy of the prediction capability are greatly enhanced when cluster analysis are performed to identify internal data structures and groupings prior to ANN modeling. Both deterministic and fuzzy-based clustering techniques are compared, and separate ANN model is constructed for each cluster. The model is then verified using a testing data set (cases that have not been used during the training stage).The proposed approach can be integrated directly into most existing reservoir management routines. In addition, incorporating techniques for dimensionality reduction and clustering with ANN demonstrates the viability of this approach for analyzing large field data set. Given that quantitative ranking of operating areas, robust forecasting, and optimization of heavy oil recovery processes are major challenges faced by the industry, the proposed research highlights the significant potential of applying effective data-driven modeling approaches in analyzing other solvent-additive steam injection projects.

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“…Artificial neural networks (ANN) are very powerful in extracting non-linear and complex relationships between input and output patterns. Several areas of application included reservoir characterization (Artun and Mohaghegh 2011;Raeesi et al 2012;Alizadeh et al 2012;Artun 2016), candidate well selection for hydraulic fracturing treatments (Mohaghegh et al 1996), field development (Centilmen et al 1999;Doraisamy et al 2000;Mohaghegh et al 1996), well-placement and trajectory optimization Rogers 2011, Guyaguler 2002;Yeten et al 2003), scheduling of cyclic steam injection processes (Patel et al 2005), screening and optimization of secondary/enhanced oil recovery (Ayala and Ertekin 2005;Artun et al 2010Artun et al , 2011bArtun et al , 2012Parada and Ertekin 2012;Amirian et al 2013), history matching (Cullick et al 2006;Silva et al 2007;Zhao et al 2015), underground-gas-storage management (Zangl et al 2006), reservoir monitoring and management (Zhao et al 2015;Mohaghegh et al 2014), and modeling of shale-gas reservoirs (Kalantari-Dhaghi et al 2015;Esmaili and Mohaghegh 2015).…”

Section: Development Of a Screening Toolmentioning

confidence: 99%

Modeling, analysis, and screening of cyclic pressure pulsing with nitrogen in hydraulically fractured wells

2016

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Cyclic pressure pulsing with nitrogen is studied for hydraulically fractured wells in depleted reservoirs. A compositional simulation model is constructed to represent the hydraulic fractures through local-grid refinement. The process is analyzed from both operational and reservoir/ hydraulic-fracture perspectives. Key sensitivity parameters for the operational component are chosen as the injection rate, lengths of injection and soaking periods and the economic rate limit to shut-in the well. For the reservoir/ hydraulic fracturing components, reservoir permeability, hydraulic fracture permeability, effective thickness and half-length are used. These parameters are varied at five levels. A full-factorial experimental design is utilized to run 1250 cases. The study shows that within the ranges studied, the gas-injection process is applied successfully for a 20-year project period with net present values based on the incremental recoveries greater than zero. It is observed that the cycle rate limit, injection and soaking periods must be optimized to maximize the efficiency. The simulation results are used to develop a neural network based proxy model that can be used as a screening tool for the process. The proxy model is validated with blind-cases with a correlation coefficient of 0.96.

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Designing cyclic pressure pulsing in naturally fractured reservoirs using an inverse looking recurrent neural network

Cited by 17 publications

References 45 publications

Characterizing Reservoir Connectivity and Forecasting Waterflood Performance Using Data-Driven and Reduced-Physics Models

Characterizing Reservoir Connectivity and Forecasting Waterflood Performance Using Data-Driven and Reduced-Physics Models

An Integrated Application of Cluster Analysis and Artificial Neural Networks for SAGD Recovery Performance Prediction in Heterogeneous Reservoirs

Modeling, analysis, and screening of cyclic pressure pulsing with nitrogen in hydraulically fractured wells

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