Full field reservoir modeling of shale assets using advanced data-driven analytics

Abstract

a b s t r a c tHydrocarbon production from shale has attracted much attention in the recent years. When applied to this prolific and hydrocarbon rich resource plays, our understanding of the complexities of the flow mechanism (sorption process and flow behavior in complex fracture systems -induced or natural) leaves much to be desired. In this paper, we present and discuss a novel approach to modeling, history matching of hydrocarbon production from a Marcellus shale asset in southwestern Pennsylvania using advanced data mining, pattern recognition and machine learning technologies. In this new approach instead of imposing our understanding of the flow mechanism, the impact of multi-stage hydraulic fractures, and the production process on the reservoir model, we allow the production history, well log, completion and hydraulic fracturing data to guide our model and determine its behavior. The uniqueness of this technology is that it incorporates the so-called "hard data" directly into the reservoir model, so that the model can be used to optimize the hydraulic fracture process. The "hard data" refers to field measurements during the hydraulic fracturing process such as fluid and proppant type and amount, injection pressure and rate as well as proppant concentration. This novel approach contrasts with the current industry focus on the use of "soft data" (non-measured, interpretive data such as frac length, width, height and conductivity) in the reservoir models. The study focuses on a Marcellus shale asset that includes 135 wells with multiple pads, different landing targets, well length and reservoir properties. The full field history matching process was successfully completed using this data driven approach thus capturing the production behavior with acceptable accuracy for individual wells and for the entire asset.Ó 2015, China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Artificial Intelligence Applications in Reservoir Engineering: A Status Check

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

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

“…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).…”