Using Artificial Intelligence in Estimating Oil Recovery Factor

Abstract

Oil and Gas companies are always concerned with continuously evaluating reserves of their assets. Calculating reserves is not an easy task since it requires full knowledge of many technical and non-technical aspects regarding the reservoir nature, available budget, utilized technology, economical conditions and others. Generally, the most important parameter in calculating the reserve for new fields/reservoirs is the "Recovery Factor". Therefore, many technical approaches available to estimate hydrocarbon reserves are related to estimation of recovery factor.Recovery factor considers all the aforementioned technical and non-technical aspects. And because these aspects can be numerous, with some that can be only quantified subjectively, the first step in our approach was to standardize and normalize many of these aspects in order to formulate them into digital values to be used in Neural Network algorithms. We gathered all those aspects and classified them into two main categories: Quantitative parameters and Qualitative parameters. Quantitative parameters are normally available in digits (e.g. permeability, porosity, net-to-gross, reservoir pressure. . . etc.). Nevertheless, this is not the case in handling the Qualitative parameters since parameters like Technology, Asset Remoteness and reservoir structure complexity are not normally represented in digits.By using wide range of actual fields' data, we had a very good calibration and control points on the algorithm we are going to use. These data include almost all the governing parameters such as Asset Economics, Technology, Facilities, Start of Production, Number of Wells, Reservoir Architecture, Rock and Fluid Properties, Reservoir Energy and others. Since some of these aspects may be hard to find, we generated two well-trained Artificial Neural Network (ANNs): (1) Simple NN includes a few "easy-toknow" basic data, and (2) Sophisticated NN that includes many advanced data with reasonable accuracy. These NNs were taught with more than 150 lessons at which the inputs (lessons) were the aforementioned "parameters" and the output was the "Recovery Factor".Testing both the simple and sophisticated networks showed prediction capabilities of 9.5% and 8.0% of actual recovery factor, respectively.

Estimation of Oil Recovery Factor for Water Drive Sandy Reservoirs through Applications of Artificial Intelligence

“…A dataset of 173 lessons was collected from literature for this study [1,9]. The datasets were analyzed statistically, and outliers were removed based on the standard deviation (SD) where any data point out of the range of ±0.3 SD was considered as an outlier.…”

“…In this study, four AI techniques-the ANNs, radial basis neuron networks (RNNs), adaptive neuro-fuzzy inference system with subtractive clustering (ANFIS-SC), and support vector machines (SVM) were used to estimate oil RF based on 10 parameters (net pay, STOIIP, the original reservoir pressure, asset area, porosity, Lorenz coefficient, effective permeability, API gravity, oil viscosity, and initial water saturation), which are readily available for all assets at their early stages; the use of these parameters was suggested recently by Noureldien and El-Banbi [1] for RF prediction using ANNs. Noureldien and El-Banbi [1] did not extract an empirical correlation from their model, while in this study, the extracted weights and biases of the optimized ANNs were used to develop an empirical equation that could be easily programmed and used for RF estimation. The predictability of the developed empirical equation will be compared with three available empirical correlations from the works of literature.…”

“…Oil recovery factor (RF) is the most significant parameter for all exploration and development (E&P) companies mainly during the early reservoir life, because several investment decisions are based on the amount of hydrocarbon, which could be obtained from the target asset with the available techniques and operational practices [1].…”

“…Currently, there are mainly six available techniques for oil reserve estimation. (1) Analogy is based on comparing the geological and petrophysical properties of poorly defined or newly discovered reservoir to old ones, and setting an oil recovery factor range for the new asset based on those of the similar assets [2]. (2) Volumetric calculations [3] calculate the stock-tank oil initially in place (STOIIP) first based on the asset dimensions, fluid properties, and rock parameters by assuming the reservoir is sealed; then, based on the recovery mechanism of the reservoir, the reserve could be estimated.…”

Classification of Reservoir Recovery Factor for Oil and Gas Reservoirs: A Multi-Objective Feature Selection Approach

“…As a result, most exploration and production firms consider the recovery factor to be a crucial metric, particularly during the reservoir's initial life. This is based on the fact that most investment choices are predicated on the quantity of hydrocarbon that can be recovered from the target inventory using present methods and operating practices [2]. Furthermore, the recovery factor indicates the recoverable hydrocarbon measured in proven reservoirs.…”