Development of Surrogate Reservoir Models (SRM) for Fast-Track Analysis of Complex Reservoirs

Abstract

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractReservoir simulation has become the industry standard for reservoir management. It is now used in all phases of field development in the oil and gas industry. The full field reservoir models that have become the major source of information and prediction for decision making are continuously updated and major fields now have several versions of their model with each new version being a major improvement over the previous one. The newer versions have the latest information (geologic, geophysical and petrophysical measurements, interpretations and calculations based on new logs, seismic data, injection and productions, etc.) incorporated in them along with adjustments that usually are the result of single-well or multi-well history matching. A typical reservoir model consists of hundreds of thousands and in many cases millions of grid blocks. As the size of the reservoir models grow the time required for each run increases. Schemes such as grid computing and parallel processing helps to a certain degree but cannot close the gap that exists between simulation runs and real-time processing. On the other hand with the new push for smart fields (a.k.a. ifields) in the industry that is a natural growth of smart completions and smart wells, the need for being able to process information in real time becomes more pronounced. Surrogate Reservoir Models (SRMs) are the natural solution to address this necessity. SRMs are prototypes of the full field models that can run in fractions of a second rather than in hours or days. They mimic the capabilities of a full field model with high accuracy. These models can be developed regularly (as new versions of the full field models become available) off-line and can be put online for automatic history matching and real-time processing that can guide important decisions. SRMs can efficiently be used for real-time optimization, real-time decision making as well as analysis under uncertain conditions. This paper presents a unified approach for development of SRMs using the state-of-the-art in intelligent systems techniques. An example for developing an SRM for a giant oil field in the Middle East is presented and the results of the analysis using the SRM for this field is discussed. In this example application SRM is used in order to analyze the impact of the uncertainties associated with several input parameters into the full field model.

Application of Well-Based Surrogate Reservoir Models (SRMs) to Two Offshore Fields in Saudi Arabia, Case Study

“…Given the fact that SRM reproduces the results of numerical simulation models in a fraction of a second and quite accurately, it is natural to see interest and curiosity in how SRMs work and how they are built. Results cited in the literature (Mohaghegh, 2006a(Mohaghegh, , 2006b(Mohaghegh, , 2006c(Mohaghegh, , 2009(Mohaghegh, , 2011(Mohaghegh, , 2012 that are mainly actual field applications of SRM and demonstrate its accuracy and effectiveness come to sharp contrast with conclusions reached by others (Zubarev, 2009) that have found the use of Neural Network in building proxy models a disappointing exercise. This is not the first time that a technology has been misused and consequently misjudged and prematurely dismissed.…”

“…Developed for the first time to replicate a mature field in the Middle East (Mohaghegh, 2006a, 2006b, 2006cand Mohaghegh, 2009, SRM can be applied to both mature and green fields. SRMs are ensemble of multiple, interconnected neuro-fuzzy systems that are trained to adaptively learn the fluid flow behavior from a multi-well, multilayer reservoir simulation model, such that it can reproduce the results similar to those of the full field reservoir simulation model (with high accuracy) in real-time (a fraction of a second per SRM run).…”

Emulation of reservoir production forecast considering variation in petrophysical properties

“…Therefore, as a substitute of the simulator that can be used to survey the uncertain space, proxy models might be applied in diverse applications within reservoir studies such as history matching (Craig et al, 1996), sensitivity analysis (Cullick et al, 2006), uncertainty assessment (Slotte et al, 2008;Mohaghegh et al, 2006), production strategy selection (Avansi et al, 2009), production forecasting and risk analysis (Amorim et al, 2012;Polizel et al, 2017).…”

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

“…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.…”