A New Screening Tool for Improved Oil Recovery Methods Using Artificial Neural Networks

Abstract

In more recent years, improved oil recovery (IOR) techniques are applied to reservoirs even before their natural energy drive is exhausted by primary depletion. Screening criteria for IOR methods are used to select the appropriate recovery technique in view of the reservoir characteristics. However, further reservoir appraisal is necessary after the applicable recovery technique is identified. The methodology proposed in this paper allows the preliminary evaluation of reservoir performance in parallel with the IOR screening process.In this study, artificial neural network (ANN) methodology is used to build a high-performance neuro-simulation tool for screening IOR methods such as waterflooding, steam injection and miscible injection of CO 2 and N 2 . This innovative tool integrates the field development plan into the screening method. The reservoir characteristics are evaluated together with a proposed production scenario to assess the most suitable recovery process and, at the same time, the reservoir performance is forecasted by providing the estimated oil production curve.The screening toolbox consists of proxy models that implement a multilayer cascade feedforward back propagation artificial network algorithm. The proxy models work for a diverse range of reservoir fluids and rock properties. The field development plan is featured in the tool by different well patterns, well spacing and well operating conditions. The ANN screening tool predicts oil production rate, cumulative oil production and estimated production time. The tool also provides the flexibility to compare the hydrocarbon production for different sets of inputs, which facilitates comparison of various depletion strategies in the screening process as well.The results of this study show that the networks are able to recognize the strong correlation between the displacement mechanism and the reservoir characteristics as they effectively forecast hydrocarbon production for different reservoirs. The tool presents a new means to design an efficient and feasible IOR project by using artificial intelligence. The proposed tool facilitates the appraisal of diverse field development strategies for oil reservoirs and allows comparison of reservoir performance under diverse production schemes.

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).…”

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

“…ANN has achieved significant popularity in areas such as production prediction (Al-Fattah & Startzman, 2003), reservoir characterization or properties prediction (An & Moon, 1993;Gharbi & Elsharkawy, 1999;Tang, Meddaugh, & Toomey, 2011), history matching (Ramgulam, 2006), classification (Stundner & Al-Thuwaini, 2001), proxy for prediction of recovery performance (Lechner & Zangl, 2005), production operation optimization and well design (Yeten, Durlofsky, & Aziz, 2002). In recent years, the neural network has also been utilized to evaluate enhanced oil recovery projects (Parada & Ertekin, 2012;Zerafat, Ayatollahi, Mehranbod, & Barzegari, 2011) and assess CO 2 sequestration process (Mohammadpoor, Firouz, Reza, & Torabi, 2012). ANN has been employed in the area of heavy oil recovery.…”

Polymer Flooding of Heavy Oil - From Screening to Full-Field Extension

“…Screening tools from the simplest to the most complicated can be used by non-experts to perform EOR screening and sometimes to forecast production and recovery (Zerafat et al 2011, Parada et al 2012, Lefebvre et al 2012, Bang 2013, Zijlstra et al 2014. Such tools are sometimes based on standard screening criteria (Taber et al 1997) but can incorporate reservoir simulations or complex correlations.…”