Intelligent Reservoir Characterization Research Articles

A Comparative Study of the Neural Network, Fuzzy Logic, and Nero-fuzzy Systems in Seismic Reservoir Characterization: An Example from Arab (Surmeh) Reservoir as an Iranian Gas Field, Persian Gulf Basin

Intelligent reservoir characterization using seismic attributes and hydraulic flow units has a vital role in the description of oil and gas traps. The predicted model allows an accurate understanding of the reservoir quality, especially at the un-cored well location. This study was conducted in two major steps. In the first step, the survey compared different intelligent techniques to discover an optimum relationship between well logs and seismic data. For this purpose, three intelligent systems, including probabilistic neural network (PNN),fuzzy logic (FL), and adaptive neuro-fuzzy inference systems (ANFIS)were usedto predict flow zone index (FZI). Well derived FZI logs from three wells were employed to estimate intelligent models in the Arab (Surmeh) reservoir. The validation of the produced models was examined by another well. Optimal seismic attributes for the estimation of FZI include acoustic impedance, integrated absolute amplitude, and average frequency. The results revealed that the ANFIS method performed better than the other systems and showed a remarkable reduction in the measured errors. In the second part of the study, the FZI 3D model was created by using the ANFIS system.The integrated approach introduced in the current survey illustrated that the extracted flow units from intelligent models compromise well with well-logs. Based on the results obtained, the intelligent systems are powerful techniques to predict flow units from seismic data (seismic attributes) for distant well location. Finally, it was shown that ANFIS method was efficient in highlighting high and low-quality flow units in the Arab (Surmeh) reservoir, the Iranian offshore gas field.

Improving the accuracy of flow units prediction through two committee machine models: An example from the South Pars Gas Field, Persian Gulf Basin, Iran

Intelligent reservoir characterization is a prerequisite study for development of oil and gas fields. Hydraulic flow units are mappable portions of hydrocarbon-bearing rocks that control fluid flow, and their modeling allows an accurate understanding of reservoir quality within a hydrocarbon field. The current study presents two committee machines based on intelligent models to make a quantitative formulation between flow units and conventional log responses in the South Pars Gas Field, Iran. First, a committee machine with intelligent systems (CMIS) is constructed using a genetic algorithm-pattern search technique. The overall mean squared error and coefficient of determination between the measured and predicted flow zone indicators (FZI) using the CMIS for the test data are 0.1468 and 0.775, respectively. Afterwards, a committee fuzzy inference system (CFIS) is constructed. For this purpose, the training data are divided into two individual clusters based on the FZI values. The two FZI clusters are trained with the individual fuzzy inference systems, and a classifier network assigns appropriate weights to each cluster. The MSE and coefficient of determination of the CFIS are 0.1233 and 0.812, respectively. The CFIS shows some improvement in the accuracy of predictions in comparison with the CMIS.The results of this study demonstrate the higher performance of the committee machines compared to the individual expert systems for estimating reservoir properties. Moreover, a primary clustering of the model data and their training with the individual expert systems can lead to a considerable improvement in the accuracy of committee machines.

Soft computing: tools for intelligent reservoir characterization (IRESC) and optimum well placement (OWP)

Soft computing: tools for intelligent reservoir characterization (IRESC) and optimum well placement (OWP)

Past, present and future intelligent reservoir characterization trends

As we approach the next millennium, and as our problems become too complex to rely only on one discipline to solve them more effectively, multi-disciplinary approaches in the petroleum industry become more of a necessity than professional curiosity. We will be forced to bring down the walls we have built around classical disciplines such as petroleum engineering, geology, geophysics and geochemistry, or at the very least, make them more permeable. Our data, methodologies and approaches to tackle problems will have to cut across various disciplines. As a result, today's “integration”, which is based on integration of results, will have to give way to a new form of integration, that is, integration of disciplines. In addition, to solve our complex problem, one needs to go beyond standard techniques and silicon hardware. The model needs to use several emerging methodologies and soft computing techniques: Expert Systems, Artificial Intelligence, Neural Network, Fuzzy Logic (GL), Genetic Algorithm (GA), Probabilistic Reasoning (PR), and Parallel Processing techniques. Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, and partial truth. Soft Computing is also tractable, robust, efficient and inexpensive. In this paper, we reveal (explore) the role of Soft Computing techniques in intelligent reservoir characterization and exploration.

Soft computing: tools for intelligent reservoir characterization (IRESC) and optimum well placement (OWP)

An integrated methodology has been developed to identify nonlinear relationships and mapping between 3-D seismic data and production log data. This methodology has been applied to a producing field. The method uses conventional techniques such as geostatistical and classical pattern recognition in conjunction with modern techniques such as soft computing (neuro-computing, fuzzy logic, genetic computing, and probabilistic reasoning). An important goal of our research is to use clustering techniques to recognize the optimal location of a new well based on 3-D seismic data and available production-log data. The classification task was accomplished in three ways; (1) k-mean clustering, (2) fuzzy c-means clustering, and (3) neural network clustering to recognize similarity cubes. Relationships between each cluster and production-log data can be recognized around the well bore and the results used to reconstruct and extrapolate production-log data away from the well bore. This advanced technique for analysis and interpretation of 3-D seismic and log data can be used to predict: (1) mapping between production data and seismic data, (2) reservoir connectivity based on multi-attribute analysis, (3) pay zone estimation, and (4) optimum well placement.