A Committee Machine with Intelligent Systems for Estimation of Total Organic Carbon Content from Petrophysical Data: an Example from Kangan and Dalan Reservoirs in South Pars Gas Field, Iran

Total Organic Carbon (TOC) content present in reservoir rocks is one of the important parameters which could be used for evaluation of residual production potential and geochemical characterization of hydrocarbon bearing units. In general, organic rich rocks are characterized by higher porosity, higher sonic transit time, lower density, higher gamma-ray, and higher resistivity than other rocks. Current study suggests an improved and optimal model for TOC estimation by integration of intelligent systems and the concept of committee machine with an example from Kangan and Dalan Formations, in South Pars Gas Field, Iran. This committee machine with intelligent systems (CMIS) combines the results of TOC predicted from intelligent systems including fuzzy logic (FL), neuro-fuzzy (NF), and neural network (NN), each of them has a weight factor showing its contribution in overall prediction. The optimal combination of weights is derived by a genetic algorithm (GA). This method is illustrated using a case study. One hundred twenty-four data points including petrophysical data and measured TOC from three wells of South Pars Gas Field were divided into eighty-seven training sets to build the CMIS model and thirty-seven testing sets to evaluate the reliability of the developed model. The results show that the CMIS performs better than any one of the individual intelligent systems acting alone for predicting TOC

Prediction of shear wave velocity from petrophysical data utilizing intelligent systems: An example from a sandstone reservoir of Carnarvon Basin, Australia

Shear wave velocity associated with compressional wave velocity can provide the accurate data for geophysical study of a reservoir. These so called petroacoustic studies have important role in reservoir characterization such as lithology determination, identifying pore fluid type, and geophysical interpretation. In this study, a fuzzy logic, a neuro-fuzzy and an artificial neural network approaches were used as intelligent tools to predict shear wave velocity from petrophysical data. The petrophysical data of two wells were used for constructing intelligent models in a sandstone reservoir of Carnarvon Basin, NW Shelf of Australia. A third well of the field was used to evaluate the reliability of the models. The results show that intelligent models have been successful for prediction of shear wave velocity from conventional well log data

A committee neural network for prediction of normalized oil content from well log data: An example from South Pars Gas Field, Persian Gulf

Normalized oil content (NOC) is an important geochemical factor for identifyingpotential pay zones in hydrocarbon source rocks. The present study proposes an optimaland improved model to make a quantitative and qualitative correlation between NOC andwell log responses by integration of neural network training algorithms and thecommittee machine concept. This committee machine with training algorithms (CMTA)combines Levenberg-Marquardt (LM), Bayesian regularization (BR), gradient descent(GD), one step secant (OSS), and resilient back-propagation (RP) algorithms. Each ofthese algorithms has a weight factor showing its contribution in overall prediction. Theoptimal combination of the weights is derived by a genetic algorithm. The method isillustrated using a case study. For this purpose, 231 data composed of well log data andmeasured NOC from three wells of South Pars Gas Field were clustered into 194modeling dataset and 37 testing samples for evaluating reliability of the models. Theresults of this study show that the CMTA provides more reliable and acceptable resultsthan each of the individual neural networks differing in training algorithms. Also CMTAcan accurately identify production pay zones (PPZs) from well logs

Petrophysical data prediction from seismic attributes using committee fuzzy inference system

This study presents an intelligent model based on fuzzy systems for making aquantitative formulation between seismic attributes and petrophysical data. The proposed methodology comprises two major steps. Firstly, the petrophysical data, including water saturation (Sw) and porosity, are predicted from seismic attributes using various Fuzzy Inference Systems (FIS), including Sugeno (SFIS), Mamdani (MFIS) and Larsen (LFIS). Secondly, a Committee Fuzzy Inference System (CFIS) is constructed using a hybrid Genetic Algorithms-Pattern Search (GA-PS) technique. The inputs of the CFIS model are the output averages of theFIS petrophysical data. The methodology is illustrated using 3D seismic and petrophysical data of 11 wells of an Iranian offshore oil field in the Persian Gulf. The performance of the CFIS model is compared with a Probabilistic Neural Network (PNN). The results show that the CFIS method performed better than neural network, the best individual fuzzy model and a simple averaging method

Unraveling the reservoir heterogeneity of the tight gas sandstones using the porosity conditioned facies modeling in the Whicher Range field, Perth Basin, Western Australia

Tight sandstones of the late Permian Willespie Formation constitute an important reservoir rock in the Whicher Range gas field of the Perth Basin. The sandstones under the effect of sedimentary conditions and diagenesis show some degree of heterogeneity reflecting in reservoir properties and production history. The Willespie Formation consists of fine to coarse-grained and gravelly feldspathic sandstones intercalated with shale, siltstone and coal, deposited in a meandering river system. Different diagenetic processes including compaction, cementation (authigenic clays, calcite and siliceous) and dissolution have severely affected the pore system properties of the reservoir sandstones, as they are considered as tight sandstones. In this study, three-dimensional modeling of reservoir sandstones has been performed using stochastic modeling algorithms for facies and porosity properties. A preliminary facies analysis of the main reservoir rocks based on core and well logs data provided the basis for reservoir zonation and modeling. Regarding the close relationship between acoustic impedance with depositional/diagenetic characteristics of reservoir facies and their porosity, this seismic attribute was used as a secondary parameter in porosity modeling. The results indicate a close relationship between sedimentary characteristics and reservoir properties. Based on the extracted models, most of the porous zones are related to the clean and coarse sandstones of the fluvial channels accumulating in the upper parts of the reservoir. In fact, initial sedimentary characteristics have the main impact on the distribution of reservoir zones, their thickness and continuity in the field and controlling large-scale reservoir heterogeneity which has been enhanced by the effect of diagenetic processes on the pore system properties and controlling the internal reservoir heterogeneity in next stages. Distinctive variability in reservoir properties towards the upper reservoir units and also among different wells can be considered for optimizing exploration and development targets of the field

Seismic inversion and attributes analysis for porosity evaluation of the tight gas sandstones of the Whicher Range field in the Perth Basin, Western Australia

A comprehensive understanding of porosity variations in tight gas sandstones plays an important role in reservoir management and provision of plans for developing of the field. This is especially important when we encounter with some degree of complexity in reservoir characteristics of these sandstones. Reservoir properties of tight gas sandstones of the Whicher Range field, the target reservoir of this study, were affected by internal reservoir heterogeneity mostly related to depositional and diagenetic features of the reservoir sandstones. In this study, 2D seismic data in combination with well log data were used for prediction of porosity based on seismic inversion technique and multi-attribute regression analysis. The results show that acoustic impedance from model based inversion is the main seismic attribute in reservoir characterization of tight sandstones of the field. Wide variations in this parameter can be effectively used to differentiate the reservoir sandstones based on their tightness degree. Investigation of porosity by this method resulted in 2D-view of porosity variations in sandstone reservoir which is in accordance with variations in geological characteristics of tight gas sandstones in the field. This view can be extended to a 3D-view in the framework of reservoir model to follow the variations throughout the field

A fuzzy logic approach for estimation of permeability and rock type from conventional well log data: an example from the Kangan reservoir in the Iran Offshore Gas Field

Permeability and rock type are the most important rock properties which can be used as input parameters to build 3D petrophysical models of hydrocarbon reservoirs. These parameters are derived from core samples which may not be available for all boreholes, whereas, almost all boreholes have well log data. In this study, the importance of the fuzzy logic approach for prediction of rock type from well log responses was shown by using an example of the Vp to Vs ratio for lithology determination from crisp and fuzzy logic approaches. A fuzzy c-means clustering technique was used for rock type classification using porosity and permeability data. Then, based on the fuzzy possibility concept, an algorithm was prepared to estimate clustering derived rock types from well log data. Permeability was modelled and predicted using a Takagi-Sugeno fuzzy inference system. Then a back propagation neural network was applied to verify fuzzy results for permeability modelling. For this purpose, three wells of the Iran offshore gas field were chosen for the construction of intelligent models of the reservoir, and a forth well was used as a test well to evaluate the reliability of the models. The results of this study show that fuzzy logic approach was successful for the prediction of permeability and rock types in the Iran offshore gas field

Integration of Adaptive Neuro-Fuzzy Inference System, Neural Networks and Geostatistical Methods for Fracture Density Modeling

Image logs provide useful information for fracture study in naturally fractured reservoir. Fracture dip, azimuth, aperture and fracture density can be obtained from image logs and have great importance in naturally fractured reservoir characterization. Imaging all fractured parts of hydrocarbon reservoirs and interpreting the results is expensive and time consuming. In this study, an improved method to make a quantitative correlation between fracture densities obtained from image logs and conventional well log data by integration of different artificial intelligence systems was proposed. The proposed method combines the results of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neural Networks (NN) algorithms for overall estimation of fracture density from conventional well log data. A simple averaging method was used to obtain a better result by combining results of ANFIS and NN. The algorithm applied on other wells of the field to obtain fracture density. In order to model the fracture density in the reservoir, we used variography and sequential simulation algorithms like Sequential Indicator Simulation (SIS) and Truncated Gaussian Simulation (TGS). The overall algorithm applied to Asmari reservoir one of the SW Iranian oil fields. Histogram analysis applied to control the quality of the obtained models. Results of this study show that for higher number of fracture facies the TGS algorithm works better than SIS but in small number of fracture facies both algorithms provide approximately same results