IOR Evaluation and Applicability Screening Using Artificial Neural Networks

Abstract

The oil industry relies heavily on predictions of the recovery processes in order to make sound operational decisions for reservoir exploitation. For planning of IOR applications, one would like to predict the performance of several competing strategies before making a decision. The main tool for performing such prediction is the reservoir simulator. The reservoir simulatorsrequire extensive information about the reservoir that may not be available or can be unreliable in the initial evaluation stage, andare quite computer intensive programs. Very often decisions on recovery strategies should be taken in an early stage of field development planning. A reliable first order screening evaluation tool enables the makimg of critical decisions on potential recovery strategies with the limited reservoir information. A multicriterion model based, on the method of Artificial Neural Networks (ANN) and interval theory, has been applied to the assessment and screening of EOR/IOR processes, such as water and gas shut off methods. An assessment relies on the existing industry experience and field practice at different reservoir conditions. It is based on applicable ranges for key reservoir parameters (permeability, porosity, depth, fluid properties, heterogeneity, rock type, salinity, etc.). Historic data are used for ANN training by Back Propagation (BP) and Scaled Conjugate Gradient (SCG) algorithms. The ANN model permits an integration of different types of data, eliminates arbitrary approach in making decisions, and provides fast computation and accuracy. The developed ANN model is capable of assessing the efficiency of a large number of EOR/IOR and well shut-off methods even when key reservoir parameters are defective or noisy. IOR Screening Today the reservoir simulator is the main tool for predicting the performance of oil recovery strategies. In order to establish a simulation model of the field a comprehensive description of the reservoir and fluids is required. Full field simulations are also time and compute intensive programs. In situations where available reservoir information is limited and fast evaluation of reservoir strategies is needed a pre-simulation screening tool can be very useful and assist in providing assessment of recovery strategies and IOR methods. Applicability of IOR methods and recovery strategies at different reservoir conditions can be assessed based on the existing field experience and IOR knowledge [1]. The multicriterion approach, based on the Artificial Neural Network (ANN) and interval theory, can be used to establish models for fast IOR applicability evaluation. Provided the expert system is populated with reliable information, the ANN tool facilitates quantified assessments of IOR methods efficiency at different reservoir conditions. ANN ANN is an information-processing system that has certain performance characteristics in common with biological neural networks. A neural network is characterized byits pattern of connections between the neurons, i.e. the network architecture,its method of determining the weights on the connections, i.e. the training (or learning) algorithm,its activation function. ANNs have been developed as generalizations of mathematical methods of human cognition or neural biology, based on the following assumptions:Information processing occurs at many simple elements called neurons.Signals are passed between neurons over connection links.Each connection link has an associated weight, which, in a typical neural net, amplifies the signal transmitted.Each neuron applies an activation function to its net input to determine its output signal.

Development of a data‐driven fuzzy screening model for enhanced oil recovery methods using an adaptive weighting system

“…The Mamdani method was used as an inference engine in the fuzzy model . Mamdani fuzzy inference is the most commonly used fuzzy methodology and was among the first control systems built using the fuzzy set theory.…”

“…The Mamdani method was used as an inference engine in the fuzzy model. [29] Mamdani fuzzy inference is the most commonly used fuzzy methodology and was among the first control systems built using the fuzzy set theory. It was proposed as an attempt to control a steam engine and boiler combination by synthesizing a set of linguistic control rules obtained from experienced human operators.…”

Data availability and development of a decision support system for heavy oil production prediction

“…Similar work was also presented in (El-M Shokir et al, 2002), which also considered the economical factors in selecting an appropriate EOR technique for a given reservoir. Surguchev and Li (2000) developed an ANN model, which can evaluate the applicability level (from 0 to 1) of the EOR technique. In addition to assisting in EOR screening, the ANN method has also been applied for predicting well performances (Zhong et al, 2001;Ferreira et al, 2012).…”

Development and Testing of Advanced Methods for the Screening of Enhanced-Oil-Recovery Techniques

“…Advanced methods take advantage of data mining strategies to assess analogy between fields, including, e.g. neural networks (Surguchev and Li, 2000;Kamari et al, 2014) or fuzzy inference (Anikin, 2014). Recent works support the use of classification and clustering analysis as effective tools for data mining in the field of EOR screening.…”