How Data-Driven Modeling Methods Like Neural Networks can Help to Integrate Different Types of Data into Reservoir Management

Day 1 Tue, June 07, 2016

2016

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...

Section: Introductionmentioning

confidence: 99%

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

Day 1 Tue, June 07, 2016

2016

“…ANN has achieved significant popularity in areas such as production prediction , reservoir characterization or properties prediction (An et al 1993, Goda et al 2003, Tang et al 2011, history matching (Ramgulam 2006), classification (Stundner et al 2001), proxy for prediction of recovery performance (Lechner et al 2005, Awoleke et al 2011, production operation optimization and well design (Stoisits et al 1999, Yeten et al 2002, Ayala H et al 2007). In recent years, the neural network has also been utilized to evaluate enhanced oil recovery (EOR) projects (Zerafat et al 2011, Parada et al 2012, predict heavy oil recoveries (Ahmadloo et al 2010, Popa et al 2012, and assess CO 2 sequestration process (Mohammadpoor et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Practical Implementation of Knowledge-Based Approaches for SAGD Production Analysis

Day 1 Tue, June 10, 2014

et al. 2014

Quantitative appraisal of different operating areas and assessment of uncertainty due to reservoir heterogeneities are crucial elements in optimization of production and development strategies in oil sands operations. Although detailed compositional simulators are available for recovery performance evaluation for SAGD, the simulation process is usually deterministic and computationally demanding, and it not quite practical for real-time decision-making and forecasting. Data mining and machine learning algorithms provide efficient modeling alternatives, particularly when the underlying physical relationships between system variables are highly complex, non-linear, and possibly uncertain.In this study, a comprehensive training set encompassing SAGD field data compiled from numerous publicly-available sources is studied. Exploratory data analysis is carried out to interpret and extract relevant attributes describing characteristics associated with reservoir heterogeneities and operating constraints. Because of their ease of implementation and computational efficiency, knowledge-based techniques including artificial neural networks (ANN) are employed to facilitate SAGD production performance prediction. Predicting (input) variables including porosity, net-to-gross ratio, saturation, gross pay, normalized shale barrier thickness and distance to well pair, and initial production rate are formulated. Measures such as cumulative production over discrete time intervals are considered as prediction (output) variables. Data records that are comprised of both input and output variables are assembled; the network is trained using the data set to identify all significant patterns and relationships that exist between the input and the output variables. The model is subsequently validated using a cross-verification procedure, during which records that have been excluded at the training stage are presented to the model. This paper demonstrates that knowledge-based techniques can be implemented in a practical manner to analyze large amount of competitor data efficiently. The approach can be integrated directly into most existing reservoir management routines. It can also be readily updated when new information has become available. Given that robust reservoir management and real-time decision-making are major challenges faced by the industry, the data-driven models presented in this paper has great potential to be applied in other recovery projects such as solvent-aided steam injection.

“…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

Day 1 Tue, June 10, 2014

et al. 2014

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.