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Shear wave velocity in the company of compressional wave velocity add up to an invaluable source of information for geomechanical and geophysical studies. Although compressional wave velocity measurements exist in almost all wells, shear wave velocity is not recorded for most of elderly wells due to lack of technologic tools in those days and incapability of recent tools in cased holes. Furthermore, measurement of shear wave velocity is to some extent costly. This study proposes a novel methodology to remove aforementioned problems by use of support vector regression tool originally invented by Vapnik (1995, The Nature of Statistical Learning Theory. Springer, New York, NY). Support vector regression (SVR) is a supervised learning algorithm plant based on statistical learning (SLT) theory. It is used in this study to formulate conventional well log data into shear wave velocity in a quick, cheap, and accurate manner. SVR is preferred for model construction because it utilizes structural risk minimization (SRM) principle which is superior to empirical risk minimization (ERM) theory, used in traditional learning algorithms such as neural networks. A group of 2879 data points was used for model construction and 1176 data points were employed for assessment of SVR model. A comparison between measured and SVR predicted data showed SVR was capable of accurately extract shear wave velocity, hidden in conventional well log data. Finally, a comparison among SVR, neural network, and four well-known empirical correlations demonstrated SVR model outperformed other methods. This strategy was successfully applied in one of carbonate reservoir rocks of Iran Gas-Fields.

Saturation pressure is one of the foremost parameters of crude oil which plays a key role in petroleum calculations. Experimentally, determination of this parameter in laboratory is costly and labor demanding. In this study, an improved intelligent model based on neural network optimized with genetic algorithm-pattern search technique is proposed for building quantitative formulation between saturation pressure and compositional data, including temperature, hydrocarbon and non-hydrocarbon compositions of crudes, and heptane-plus specifications. Genetic algorithm-pattern search technique is embedded in neural network formulation for finding optimal weights and biases of neural network. A comparison among the proposed model and published models in literature reveals the superiority of our model in terms of better accuracy and higher generalization. Improved neural networked showed R-square of 0.9892 and MSE of 17,617.99 which concludes that it is a promising alternative for determination of saturation pressure which is able to eliminating expenses of laboratory measurements and significantly saving time. This study showed GA considerably enhanced performance of conventional neural networks.

Precipitation and deposition of asphaltene have undesirable effects on the petroleum industry by increasing operational costs due to reduction of well productivity as well as catalyst poisoning. Herein we propose a reliable model for quantitative estimation of asphaltene precipitation. Scaling equation is the most powerful and popular model for accurate prediction of asphaltene precipitated out of solution in crudes without regard to complex nature of asphaltene. We employed a new mathematical-based approach known as alternating conditional expectation (ACE) technique for combining results of different scaling models in order to increase the accuracy of final estimation. Outputs of three well-known scaling equations, including Rassamdana (RE), Hu (HU), and Ashoori (AS), are input to ACE and the final output is produced through a nonlinear combination of scaling equations. The proposed methodology is capable of significantly increasing the precision of final estimation via a divide-and-conquer principle in which ACE functions as the combiner. Results indicate the superiority of the proposed method compared with other individual scaling equation models.

Bubble point pressure (Pb), one of the most important parameters of reservoir fluids, plays an important role in petroleum engineering calculations. Accurate determination of Pb from laboratory experiments is time, cost and labor intensive. Therefore, the quest for an accurate, fast and cheap method of determining Pb is inevitable. In this communication, a sophisticated approach was followed for formulating Pb to temperature, hydrocarbon and non-hydrocarbon compositions of crudes, and heptane-plus specifications. Firstly, support vector regression (SVR), a supervised learning algorithm plant based on statistical learning (SLT) theory, was employed to construct a model estimating Pb. Subsequently, an alternating conditional expectation (ACE) was used to transform input/output data space to a highly correlated data space and consequently to develop a strong formulation among them. Eventually, SVR and ACE models are combined in a power-law committee machine structure by virtue of genetic algorithm to enhance accuracy of final prediction. A comparison among constructed models and previous models using the concepts of correlation coefficient, mean square error, average relative error and absolute average relative error reveals power-law committee machine outperforms all SVR, ACE, and previous models.

Accurate determination of oil bubble point pressure (Pb) from laboratory experiments is time, cost and labor intensive. Therefore, the quest for an accurate, fast, and cheap method of determining Pb is inevitable. Since support vector based regression satisfies all components of such a quest through a supervised learning algorithm plant based on statistical learning theory, it was employed to formulate available PVT data into Pb. Open-sources literature data were used for SVR model construction and Iranian Oils data were employed for model evaluation. A comparison among SVR, neural network and three well-known empirical correlations demonstrated superiority of SVR model.

Shear wave velocity provides invaluable information for geomechanical, geophysical, and reservoir characterization studies. However, measurement of shear wave velocity is time, cost and labor intensive. This study proposes a swift and exact methodology, called ACE stimulated neural network, for prediction of shear wave velocity from available well logs such that it will be able to surpass previous models. The proposed method is composed of two major parts: 1) transforming input/output data space to a higher correlated space using alternative condition expectation (ACE), and 2) making a neural network formulation in transformed data space. Transforming in the first step makes it easier for neural network to find the complicated underlying dependency of input/output data. Therefore, neural network will be able to develop an accurate and strong formulation between conventional well logs and shear wave velocity. The Propounded approach was successfully applied in one of the carbonate gas fields of Iran. A comparison between proposed model and previous models showed superiority of ACE stimulated neural network.

Poisson's ratio is one of the most important rock mechanical parameters having significance in both planning and post analysis of wellbore operations. Laboratory measurement of this parameter covers a broad range of costs, including sidewall sampling, preservation, and laboratory tests. This study proposes an improved strategy, called dual stimulated fuzzy logic by ACE and GA-PS for determining Poisson's ratio from conventional well log data in a rapid, precise, and cost-effective way. Firstly, conventional well log data are transformed to a higher correlated data space with Poisson's ratio through the use of alternative condition expectation (ACE) algorithm. This step simplifies the convoluted space of the problem and makes it easier to solve for fuzzy logic. Subsequently, transformed conventional well log data are fed to fuzzy logic model. To ensure that optimal fuzzy model is constructed, a hybrid genetic algorithm-pattern search (GA-PS) technique is employed for extracting fuzzy clusters (or rules). This step sets fuzzy logic to its optimal performance. The propounded strategy was successfully applied to data from carbonate reservoir rocks of an Iranian Oil Field. A comparison between present model and previous models showed superiority of current study.

Saturation pressure is critical parameter of reservoir fluids which significantly affects petroleum engineering calculations. Accurate measurement of saturation pressure from laboratory experiment is very time, cost, and labor intensive. Therefore, it is favorable in most cases to achieve this parameter from empirical correlations. Three well-known models for estimation of saturation pressure from gas chromatography data include Elsharkawy model (EM), Soave–Redlich–Kwong (SRK), and Peng–Robinson (PR) equations of states (EOSs). This model proposes a novel approach, called committee machine, to reap beneficial advantages aforementioned three models through the combination of them. Committee machine produces a sophisticated model which performs in cooperation of EM, SRK and PR EOSs. Results indicated that CM model enhanced the accuracy of final prediction and performed more satisfyingly compared with individual model acting alone.

Quantitative formulation between conventional well log data and rock permeability, undoubtedly the most critical parameter of hydrocarbon reservoir, could be a potent tool for solving problems associated with almost all tasks involved in petroleum engineering. The present study proposes a novel approach in charge of the quest for high-accuracy method of permeability prediction. At the first stage, overlapping of conventional well log data (inputs) was eliminated by means of principal component analysis (PCA). Subsequently, rock permeability was predicted from extracted PCs using multi-layer perceptron (MLP), radial basis function (RBF), and generalized regression neural network (GRNN). Eventually, a committee neural network (CNN) was constructed by virtue of genetic algorithm (GA) to enhance the precision of ultimate permeability prediction. The values of rock permeability, derived from the MPL, RBF, and GRNN models, were used as inputs of CNN. The proposed CNN combines results of different ANNs to reap beneficial advantages of all models and consequently producing more accurate estimations. The GA, embedded in the structure of the CNN assigns a weight factor to each ANN which shows relative involvement of each ANN in overall prediction of rock permeability from PCs of conventional well logs. The proposed methodology was applied in Kangan and Dalan Formations, which are the major carbonate reservoir rocks of South Pars Gas Field-Iran. A group of 350 data points was used to establish the CNN model, and a group of 245 data points was employed to assess the reliability of constructed CNN model. Results showed that the CNN method performed better than individual intelligent systems performing alone.

The portion of rock pore volume occupied with non-hydrocarbon fluids is called water saturation, which plays a significant role in reservoir description and management. Accurate water saturation, directly measured from special core analysis is highly expensive and time consuming. Furthermore, indirect measurements of water saturation from well log interpretation such as empirical correlations or statistical methods do not provide satisfying results. Recent works showed that fuzzy logic is a robust tool for handling geosciences problems which provide more reliable results compared with empirical correlations or statistical methods. This study goes further to improve fuzzy logic for enhancing accuracy of final prediction. It employs hybrid genetic algorithm-pattern search technique instead of widely held subtractive clustering approach for setting up fuzzy rules and for extracting optimal parameters involved in computational structure of fuzzy model. The proposed strategy, called genetic implanted fuzzy model, was used to formulate conventional well log data, including sonic transit time, neutron porosity, formation bulk density, true resistivity, and gamma ray into water saturation, obtained from subtractive clustering approach. Results indicated genetic implanted fuzzy model performed more satisfyingly compared with traditional fuzzy logic model. The propounded model was successfully applied to one of Iranian carbonate reservoir rocks.

A major concern in the petroleum industry is asphaltene precipitation, which has negative impacts on production costs and recovery. The scaling equation is the most popular approach for modeling asphaltene precipitated out of solution in crude oils. Due to different values assigned for involved coefficients in scaling equations, they might overestimate or underestimate in some region relative to each other. This study proposes an improved strategy for tuning scaling equations and compensating effects of overestimation and underestimation through fuzzy rules. This strategy, called fuzzy tuning of scaling equations (FTSE), has a parallel framework, which gains outputs of different scaling equations and then introduces them to a fuzzy model as inputs. The fuzzy model breaks down the problem into subspaces through fuzzy membership functions and solves each region separately using fuzzy rules. Aggregating results of each subspace produces final model's output (i.e., FTSE output). Results indicated that FTSE pe...

Knowledge about stability of asphaltene, determined by difference index, is of significant interest because of the many problems associated with asphaltene precipitation. This study followed two parallel fuzzy strategies for estimating refractive index (RI) of crude oil and refractive index of crude oil at onset of asphaltene precipitation (PRI) from Sara fraction data. Predicted RI and PRI were then utilized for easy and fast diagnosis of asphaltene stability by dint of calculating difference index (or ΔRI = RI – PRI). The experimental data reported in the literature have been used for model developing and checking. An acceptable agreement between fuzzy predicted values and experimental data confirmed the power of fuzzy logic technique in prediction of RI, PRI, and consequent ΔRI. In this study, ΔRI was not predicted directly mainly for two reasons. First, RI and PRI contain invaluable information themselves and predicting them fulfills the need for these information when they are desired. Second, dividi...

The scaling equation is the most popular mathematical modeling of asphaltene precipitation as a problematic issue in petroleum industry. There are eight adjustable coefficients in the scaling equation that govern the quality of the fit between titration data and the scaling equation model. In this study, a hybrid genetic algorithm-pattern search (GA-PS) tool was employed to extract optimal values of the involved coefficients in the scaling equation through the stochastic search. For better performance of the GA-PS tool, dimensionality of the problem was broken into two simpler parts using the divide-and-conquer principle by introducing two fitness functions. The renovated scaling equation was compared with previous works; it was shown that the proposed method outperforms previous works.

Precipitation and deposition of asphaltene during different stages of petroleum production is recognized as problematic in oil industry because of the increase in production cost and the inhibition of a consistent flow of crude oil in different medium. Numerous correlations have been developed to determine asphaltene stability in crude oil. In this study, a novel ONN method was used to estimate difference index from SARA fraction data for rapid, accurate, and cost-effective determination of asphaltene stability. Neural networks are highly in danger of trapping in local minima. To eliminate this flaw, a hybrid genetic algorithm-pattern search technique was used instead of common back-propagation algorithm for training the employed neural network. A comparison between neural network and optimized neural network indicated superiority of optimized neural network.

Oil-CO2 minimum miscible pressure (MMP) has significance in selecting appropriate reservoir for miscible gas injection and greatly governs performance of local displacement. Accurate determination of MMP is very expensive, time-consuming, and labor intensive. Therefore, the quest for a method to determine MMP accurately and save time and money is necessary. This study held a competition between neural network and support vector regression models and assessed their performance in prediction of MMP for both pure and impure miscible CO2 injection. Subsequently, a committee machine was constructed based on divide and conquer principle to reap benefits of both model and increases the precision of final prediction. Results indicated committee machine performed more satisfyingly compared with individual intelligent models performing alone.

Deposition of asphaltene during crude oil production is a challenging issue in oil industry which causes considerable loss of production efficiency as well as imposes negative impacts on production rates. Upon variation in pressure, temperature and crude oil composition, asphaltene begins to precipitate and deposits in reservoir rock and consequently causes formation damage owing to mechanisms of wettability alteration and pore throat blockage. In the present study a sophisticated method, called committee machine with optimized intelligent systems was utilized to predict the amount of asphaltene precipitation from experimental titration data. The committee machine is composed of optimized neural network and optimized fuzzy logic. Stochastic optimization of neural network and fuzzy logic by virtue of hybrid genetic algorithm-pattern search technique significantly enhances their efficiencies. The committee machine provides a further improvement in accuracy of final prediction through integrating optimized intelligent systems and consequent reaping of their benefits. The committee machine model was applied to experimental data reported in the open-source literature. It was observed that there was an acceptable agreement between experimental data and committee machine predicted values. Finally, performance of committee machine model was compared with other intelligent systems used for prediction of asphaltene precipitation. Results showed superiority of committee machine in asphaltene precipitation modeling to optimized neural network and optimized fuzzy logic.

Oil formation volume factor (FVF) refers to the change in oil volume between reservoir and standard conditions at surface. It is a crucial oil property which is governed by reservoir temperature, amount of dissolved gas in oil, and specific gravity of oil and dissolved gas. This parameter plays a trivial role in petroleum reservoir and production calculations. Accurate determination of oil FVF is restricted by limitations on reliable sampling and high cost and time-consumption associated with laboratory experiments. Furthermore, available empirical correlations do not have satisfying generalization and accuracy owing to being calibrated on specific oil samples. Therefore, this study offers a Takagi–Sugeno (TS) fuzzy logic model for estimating oil FVF for the purpose of developing a precise model calibrated on regional Iranian oil using 367 training samples. TS fuzzy model utilizes subtractive clustering approach for determining number of rules and clusters. Evaluation of constructed fuzzy logic using 108 unseen test data points indicated achievement of fuzzy logic in prediction of oil FVF.

Oil formation volume factor (FVF) is considered as relative change in oil volume between reservoir condition and standard surface condition. FVF, always greater than one, is dominated by reservoir temperature, amount of dissolved gas in oil, and specific gravity of oil and dissolved gas. In addition to limitations on reliable sampling, experimental determination of FVF is associated with high costs and time-consumption. Therefore, this study proposes a novel approach based on hybrid genetic algorithm-pattern search (GA-PS) optimized neural network (NN) for fast, accurate, and cheap determination of oil FVF from available measured pressure-volume-temperature (PVT) data. Contrasting to traditional neural network which is in danger of sticking in local minima, GA-PS optimized NN is in charge of escaping from local minima and converging to global minimum. A group of 342 data points were used for model construction and a group of 219 data points were employed for model assessment. Results indicated superiority of GA-PS optimized NN to traditional NN. Oil FVF values, determined by GA-PS optimized NN were in good agreement with reality.

Porosity, the void portion of reservoir rocks, determines the volume of hydrocarbon accumulation and has a great control on assessment and development of hydrocarbon reservoirs. Accurate determination of porosity from core analysis is highly cost, time, and labor intensive. Therefore, the mission of finding an accurate, fast and cheap way of determining porosity is unavoidable. On the other hand, conventional well log data, available in almost all wells contain invaluable implicit information about the porosity. Therefore, an intelligent system can explicate this information. Fuzzy logic is a powerful tool for handling geosciences problem which is associated with uncertainty. However, determination of the best fuzzy formulation is still an issue. This study purposes an improved strategy, called hybrid genetic algorithm–pattern search (GA–PS) technique, against the widely held subtractive clustering (SC) method for setting up fuzzy rules between core porosity and petrophysical logs. Hybrid GA–PS technique is capable of extracting optimal parameters for fuzzy clusters (membership functions) which consequently results in the best fuzzy formulation. Results indicate that GA–PS technique manipulates both mean and variance of Gaussian membership functions contrary to SC that only has a control on mean of Gaussian membership functions. A comparison between hybrid GA–PS technique and SC method confirmed the superiority of GA–PS technique in setting up fuzzy rules. The proposed strategy was successfully applied to one of the Iranian carbonate reservoir rocks.

Nuclear Magnetic Resonance (NMR) logging provides priceless information about hydrocarbon bearing intervals such as free fluid porosity and permeability. This study focuses on using geostatistics from NMR logging instruments at high depths of investigation to enhance vertical resolution for better understanding of reservoirs. In this study, a NMR log was used such that half of its midpoint data was used for geostatistical model construction using an ordinary kriging technique and the rest of the data points were used for assessing the performance of the constructed model. This strategy enhances the resolution of NMR logging by twofold. Results indicated that the correlation coefficient between measured and predicted permeability and free fluid porosity is equal to 0.976 and 0.970, respectively. This means that geostatistical modeling is capable of enhancing the vertical resolution of NMR logging. This study was successfully applied to carbonate reservoir rocks of the South Pars Gas Field.

Poisson ratio is considered as one of the most important rock mechanical properties of hydrocarbon reservoirs. Determination of this parameter through laboratory measurement is time, cost, and labor intensive. Furthermore, laboratory measurements do not provide continuous data along the reservoir intervals. Hence, a fast, accurate, and inexpensive way of determining Poisson ratio which produces continuous data over the whole reservoir interval is desirable. For this purpose, support vector regression (SVR) method based on statistical learning theory (SLT) was employed as a supervised learning algorithm to estimate Poisson ratio from conventional well log data. SVR is capable of accurately extracting the implicit knowledge contained in conventional well logs and converting the gained knowledge into Poisson ratio data. Structural risk minimization (SRM) principle which is embedded in the SVR structure in addition to empirical risk minimization (EMR) principle provides a robust model for finding quantitative formulation between conventional well log data and Poisson ratio. Although satisfying results were obtained from an individual SVR model, it had flaws of overestimation in low Poisson ratios and underestimation in high Poisson ratios. These errors were eliminated through implementation of fuzzy classifier based SVR (FCBSVR). The FCBSVR significantly improved accuracy of the final prediction. This strategy was successfully applied to data from carbonate reservoir rocks of an Iranian Oil Field. Results indicated that SVR predicted Poisson ratio values are in good agreement with measured values.

Porosity of hydrocarbon bearing formations is a crucial parameter for reservoir characterization, reserve estimation, planning for completion, and geomechanical and geophysical studies. Accurate determination of porosity from laboratory core analysis is highly cost, time, and people intensive. Therefore, the quest for a rapid, cost-effective, and efficient method of determining porosity is inevitable. Conventional well log data are available in all wells and provide cheap continuous information. In this study, an improved strategy was followed to formulate conventional well log data (inputs) into core porosity (output) using the genetic optimized neural network (GONN). Firstly, back-propagation (BP) algorithm, the conventional learning method of neural network, was used to extract the formulation between inputs/output data space. Then, neural network was trained through the use of genetic algorithm (GA). Comparison between BP learning and GA demonstrated the effectiveness of GONN. It was deduced that GA enforces the performance function of neural network to converge to global minimum contrary to BP which frequently traps in local minima. General Terms

Bubble point pressure is the most crucial pressure–volume–temperature (PVT) property of reservoir fluid, which plays a critical role in almost all tasks related to reservoir and production engineering. Therefore, an accurate, quick, and easy way of predicting bubble point pressure from available PVT parameters is desired. In this study, an improved methodology is followed for making a quantitative formulation between bubble point pressure (target) and some available PVT data (inputs) such as proportion of solution gas–oil-ratio over gas gravity, temperature, and stock-tank oil gravity. At the first stage of this research, bubble point pressure was predicted from PVT data using different intelligent systems, including neural network, fuzzy logic, and neuro-fuzzy algorithms. Subsequently, a power-law committee with intelligent systems was constructed by virtue of hybrid genetic algorithm-pattern search tool. The proposed methodology, power-law committee with intelligent systems, comprises a parallel framework that produces a final output by combining the results of individual intelligent systems. To achieve this objective, a power-law formula structure was designated to integrate outputs of intelligent systems. A hybrid genetic algorithm-pattern search tool was then employed to find the optimal coefficients of this formula. A database of 361 worldwide data points was employed in this study, while 282 data points were used for model construction (i.e., training data), and 79 data points were employed to assess the reliability of the model (test data). Results showed that outputs of intelligent systems are in good agreement with reality. However, by little additional computation, power-law committee with intelligent systems is capable of significantly improving the accuracy of target prediction.

Measurement of compressional, shear, and Stoneley wave velocities, carried out by dipole sonic imager (DSI) logs, provides invaluable data in geophysical interpretation, geomechanical studies and hydrocarbon reservoir characterization. The presented study proposes an improved methodology for making a quantitative formulation between conventional well logs and sonic wave velocities. First, sonic wave velocities were predicted from conventional well logs using artificial neural network, fuzzy logic, and neuro-fuzzy algorithms. Subsequently, a committee machine with intelligent systems was constructed by virtue of hybrid genetic algorithm-pattern search technique while outputs of artificial neural network, fuzzy logic and neuro-fuzzy models were used as inputs of the committee machine. It is capable of improving the accuracy of final prediction through integrating the outputs of aforementioned intelligent systems. The hybrid genetic algorithm-pattern search tool, embodied in the structure of committee machine, assigns a weight factor to each individual intelligent system, indicating its involvement in overall prediction of DSI parameters. This methodology was implemented in Asmari formation, which is the major carbonate reservoir rock of Iranian oil field. A group of 1,640 data points was used to construct the intelligent model, and a group of 800 data points was employed to assess the reliability of the proposed model. The results showed that the committee machine with intelligent systems performed more effectively compared with individual intelligent systems performing alone.

Shear wave velocity is a critical physical property of rock, which provides significant data for geomechanical and geophysical studies. This study proposes a multi-step strategy to construct a model estimating shear wave velocity from conventional well log data. During the first stage, three correlation structures, including power law, exponential, and trigonometric were designed to formulate conventional well log data into shear wave velocity. Then, a Genetic Algorithm-Pattern Search tool was used to find the optimal coefficients of these correlations. Due to the different natures of these correlations, they might overestimate/underestimate in some regions relative to each other. Therefore, a neuro-fuzzy algorithm is employed to combine results of intelligently derived formulas. Neuro-fuzzy technique can compensate the effect of overestimation/underestimation to some extent, through the use of fuzzy rules. One set of data points was used for constructing the model and another set of unseen data points was employed to assess the reliability of the propounded model. Results have shown that the hybrid genetic algorithm-pattern search technique is a robust tool for finding the most appropriate form of correlations, which are meant to estimate shear wave velocity. Furthermore, neuro-fuzzy combination of derived correlations was capable of improving the accuracy of the final prediction significantly.