Application of support vector regression to estimate the formation water saturation in one of the largest oil fields located in the southwest of Iran

Summary: Water saturation (Sw) of a hydrocarbon reservoir is an important petrophysical parameter having a great impact on the accuracy of primitive estimation of the reservoir. Due to highly importance of this parameter dealing with the economic calculations of the reservoir, it must be estimated precisely. Although experimental analysis of core samples taken from a reservoir leads to very useful information about Sw of the reservoir, this experimental method is highly expensive and time consuming; and therefore, this method is applicable only for a small number of wells in a field. To overcome this problem, an intelligent pattern recognition method, known as support vector regression (SVR), has been employed in the current research to estimate Sw from well logs data of 3 wells in one of the largest oil fields of Iran. The performance of the algorithm has also been validated through different criteria. The results of this research indicate that the SVR model can estimate Sw from well logs data accurately, in which the determination coefficients of 87 and 76 percent have been obtained from the training and test steps, respectively. Introduction: Generally in most commonly hydrocarbon reservoirs, Sw is estimated using well logs data through applying Archie's fundamental empirical relation. However, this relation is just satisfied for clean sandstone formations (without clay minerals). So far several empirical models have been proposed to measure Sw using well logs data. The main disadvantage of these models is their formation dependency, which makes the models specific and not comprehensive to be applied in a variety of other formations. In addition to empirical methods, several linear regression techniques have also been applied to estimate this parameter using well logs data. These techniques cannot estimate Sw appropriately due to the complexity of the parameter features. Resistivity and porosity logs are the most important well logs used to estimate Sw by Archie's relation. The porosity of a formation can be very accurately determined through sonic, density and neutron logs. However, resistivity logs are very sensitive to the presence of shale and other clayey impurities in formations. Their effects can be adjusted by means of gamma ray (GR) log. Therefore, to estimate Sw,, employimg an intelligent method using appropriate well logs data will be useful. The oil reservoir, studied in this research, is located in Asmari formation in southwest of Zagros Mountain. Overall this formation in the investigated region has been formed from a sequence comprising of carbonate rocks (limestone and dolomite), sandstone and shale. Methodology and Approaches: In the current research, to estimate Sw, SVR method has been applied to well logs data from 3 wells in one of the largest oil fields of Iran. In this study, appropriate well logs data comprising of GR, neutron porosity, formation bulk density, sonic transit time and true resistivity from deep induction log (ILD) have been used. Moreover, Sw values measured from cores in the laboratory are available for whole depth of the wells. In order to employ SVR to estimate Sw, the model needs to be trained using appropriate input and output data in MATLAB environment. In the current research, the input consists of 5 variables (well logs data) while the output is only the Sw parameter. From 1211 data points (containing 5 variables of well logs data and Sw parameter measured by core) available from the 3 wells, about 80 percent (i.e. 988 samples) were selected for training and the remaining 20 percent (i.e. 223 samples) were chosen for test. To compare the estimated values with the measured ones for the reservoir in the study region, visually, chart of lithology, water and hydrocarbon saturations of the formation were also depicted for the 3 wells by means of Geolog software. Results and Conclusions: The performance of the algorithm has been validated through different criteria such as scatter plot of Sw values from cores versus the estimated Sw values from well logs data of 3 study wells by means of SVR model as well as computing statistical parameters indicating the accuracy of the results. Furthermore, the results of the research revealed that the SVR model can estimate Sw using well logs data accurately so that it has estimated the training and test data with the determination coefficients of 87 and 76 percent, respectively. As a result, the proposed method, i.e. SVR, is an accurate, fast and cost-effective method to evaluate the petrophysical parameter Sw.