Fatai Anifowose - Academia.edu (original) (raw)
Papers by Fatai Anifowose
79th EAGE Conference and Exhibition 2017 - Workshops, 2017
An approach, comprising statistical and artificial intelligence techniques, to modeling rock ceme... more An approach, comprising statistical and artificial intelligence techniques, to modeling rock cementation factor in a Saudi Arabian carbonate reservoir using wireline logs is presented. The objective is to obtain a more accurate prediction of rock cementation factor, denoted by the exponent, m, in Archie’s equation, as a variable log using multivariate linear regression (MLR), artificial neural networks, and support vector machines. Published equations by Nugent, Lucia and Shell are empirical derivations based on porosity logs and assumptions that may not be applicable in other geological settings. Typically, log analysts use the average of m values obtained from special core analysis (SCAL) measurements. Such constant values do not account for formation heterogeneity resulting in inaccurate water saturation and pore volume estimation with high operational and economic costs. In this study, six wireline logs from seven wells were combined with their corresponding core measured m values to build and optimize the proposed models to predict the m values for new wells or uncored sections of existing wells. The predicted m values produced by the MLR model closely matched available m data from SCAL measurements. This study fulfills the pressing need for variable m as a more accurate input to water saturation models.
First EAGE Digitalization Conference and Exhibition, 2020
Summary Grain size is a key input to various reservoir models. The models require a continuous lo... more Summary Grain size is a key input to various reservoir models. The models require a continuous log of grain size. Core samples are usually not available over the entire reservoir section. The most accurate grain size measurement is obtained from sieve and laser particle size analyses. These methods are expensive. The conventional method, the visual core description, is time-consuming, subjective, and nonreproducible. Alternative methods include the use of empirical equations, nuclear magnetic resonance (NMR) relaxation time, and acoustic velocities. These latter methods require inputs that are not sufficiently available, not applicable to different geological settings, or not available for all wells. This paper proposes a new methodology that estimates reservoir rock grain size for a new well or reservoir section from archival core description data and their corresponding wireline logs using machine learning technology. Nine wells from a clastic reservoir are used. Seven wells are combined to build the training set while the remaining two are used for model validation. Three machine learning methods are implemented and trained with optimized parameters. The results showed that, despite the subjectivity and bias associated with the core description data, the machine learning methods are capable of estimating the grain size for the validation wells.
Day 2 Tue, November 01, 2022
Sonic logs are important for deriving elastic moduli of rocks, which can be useful in calculating... more Sonic logs are important for deriving elastic moduli of rocks, which can be useful in calculating in-situ stresses, estimating safe drilling mud weight, controlling wellbore stability, and constructing velocity models for seismic processing. Practically, determining the geomechanical information of the subsurface in real-time can alleviate operational risks and improve formation evaluation. Since sonic logs are not acquired in real-time, machine learning can be utilized to estimate them in real-time using drilling parameters and mud gas data. This study uses Random Forest machine learning technique to predict compressional wave slowness in real-time by utilizing surface drilling parameters and mud gas data. Out of a total of five wells, the regression model is trained with data from four wells. The input parameters for each depth point include conventional drilling parameters (rate of penetration, torque, weight on bit, etc.) and mud gas data. Various preprocessing techniques were a...
Journal of Petroleum Exploration and Production Technology, 2021
Seismic velocity modeling is a crucial step in seismic processing that enables the use of velocit... more Seismic velocity modeling is a crucial step in seismic processing that enables the use of velocity information from both seismic and wells to map the depth and thickness of subsurface layers interpreted from seismic images. The velocity can be obtained in the form of normal moveout (NMO) velocity or by an inversion (optimization) process such as in full-waveform inversion (FWI). These methods have several limitations. These limitations include enormous time consumption in the case of NMO due to manual and heavy human involvement in the picking. As an optimization problem, it incurs high cost and suffers from nonlinearity issues. Researchers have proposed various machine learning (ML) techniques including unsupervised, supervised, and semi-supervised learning methods to model the velocity more efficiently. The focus of the studies is mostly to automate the NMO velocity picking, improve the convergence in FWI, and apply FWI using ML directly from the data. In the purview of the digita...
This paper presents a comparative study of the performance of three versions of Adaptive Neuro-Fu... more This paper presents a comparative study of the performance of three versions of Adaptive Neuro-Fuzzy Inference System (ANFIS) hybrid model and two innovative hybrid models in the prediction of oil and gas reservoir properties. ANFIS is a hybrid learning algorithm that combines the rule-based inferencing of fuzzy logic and the back-propagation learning procedure of Artificial Neural Networks. Functional Networks-Support Vector Machines (FN-SVM) and Functional Networks-Type-2 Fuzzy Logic (FN-T2FL) were proposed to improve the performance of the stand-alone SVM and T2FL models respectively. The FN component of the FN-T2FL hybrid model automatically extracts the most relevant attributes from the input data using the least square fitting algorithm as an improvement over the individual Functional Networks and Type-2 Fuzzy Logic models. The former is more promising as it combines two existing techniques that are very close in performance and well known for their computational stability and fast processing. The FN-SVM hybrid model also benefits from the excellent performance of the least-square-based model-selection algorithm of Functional Networks and the non-linear high-dimensional feature transformation capability that is based on structural risk minimization and Tikhonov regularization properties of SVM. Training and testing the SVM component of the hybrid model with the best and dimensionally-reduced variables from the input data resulted in better performance with higher correlation coefficients, lower root mean square errors and less execution time than the traditional SVM model. A comparison of FN-SVM and FN-T2FL with the three versions of ANFIS showed the superiority of the FN-SVM model over the others. The three ANFIS models still proved to be good in solving real industrial problems due to their speed of execution especially in dense data conditions.
SPE Middle East Oil and Gas Show and Conference, 2013
ABSTRACT This paper presents a comparative study of the capabilities of Extreme Learning Machines... more ABSTRACT This paper presents a comparative study of the capabilities of Extreme Learning Machines (ELM), Decision Trees (DT) and Artificial Neural Networks (ANN), in the prediction of permeability from specific surface area, porosity and water saturation. ANN has been applied in the prediction of various oil and gas properties but with limitations such as computational instability due to its lack of global optima. ELM and DT are recent advances in Artificial Intelligence with improved architectures and better performance. The techniques were optimized and applied to the same carbonate reservoir field dataset . Following the popular convention and to ensure fairness, a stratified sampling approach was used to randomly extract 70% of the dataset for training while the remaining 30% was used for testing. The results showed that ELM performed best with the highest correlation coefficient, lowest root mean square error and shortest execution time. This agrees perfectly with the literature that ELM has a more compact architecture optimized for faster execution than the original ANN. DT was also found to be a promising technique for reservoir modeling. The results showed that ELM performed best with the highest correlation coefficient, lowest root mean square error and shortest execution time. This agrees perfectly with the literature that ELM has a more compact architecture optimized for faster execution than the original ANN. DT was also found to be a promising technique for reservoir modeling.
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 2015
System availability is a key performance measure in the process industry. It ensures continuous o... more System availability is a key performance measure in the process industry. It ensures continuous operation of facilities to meet production targets, personnel safety and environmental sustainability. Process machinery condition assessment, early fault detection and its management are vital elements to ensure overall system availability. These elements can be explored and managed effectively by extracting hidden knowledge from machinery vibration information to improve plant availability and safe operations. This article describes a decision tree–based computational intelligence model using machinery vibration data to detect machinery faults, their severity, and suggests appropriate action to avoid unscheduled failures. Vibration data for this work were collected using a machinery simulator and real-world machine to show the applicability of the proposed model. Later, the data were analyzed to detect faults using decision tree–based model that was developed in MATLAB. Fault detection classification accuracies of 98% during training and 93% during testing showed excellent performance of the proposed model. The model also revealed that the proposed formulation has capability of detecting faults correctly in the range of 98%−99%. The results showed that the proposed decision tree–based model is effective in evaluating the condition of process machinery and predicting unscheduled equipment breakdowns with better accuracy and with reduced human effort.
Expert Systems with Applications, 2010
ABSTRACT The process of combining multiple computational intelligence techniques to build a singl... more ABSTRACT The process of combining multiple computational intelligence techniques to build a single hybrid model has become increasingly popular. As reported in the literature, the performance indices of these hybrid models have proved to be better than the individual components when used alone. Hybrid models are extremely useful for reservoir characterization in petroleum engineering, which requires high-accuracy predictions for efficient exploration and management of oil and gas resources.In this paper, we have utilized the capabilities of data mining and computational intelligence in the prediction of porosity and permeability, two important petroleum reservoir characteristics, based on the hybridization of Fuzzy Logic, Support Vector Machines, and Functional Networks, using several real-life well-logs. Two hybrid models have been built. In both, Functional Networks were used to select the best of the predictor variables for training directly from input data by using its functional approximation capability with least square fitting algorithm. In the first model (FFS), the selected predictor variables were passed to Type-2 Fuzzy Logic System to handle uncertainties and extract inference rules, while Support Vector Machines made the final predictions. In the second, the selected predictor variables were passed to Support Vector Machines for training by transforming them to a higher dimensional space, and then to Type-2 Fuzzy Logic to handle uncertainties, extract inference rules and make final predictions.The simulation results show that the hybrid models perform better than the individual techniques when used alone for the same datasets with their higher correlation coefficients. In terms of execution time, the hybrid models took less time for both training and testing than the Type-2 Fuzzy Logic, but more time than Functional Networks and Support Vector Machines. This could be the price for having a better and more robust model. The hybrid models also performed better than a combination of two of the individual components, Type-2 Fuzzy Logic and Support Vector Machines, in terms of higher correlation coefficients as well as lower execution times. This is due to the effective role of Functional Networks, as a best-variable selector in the hybrid models.
Journal of Experimental & Theoretical Artificial Intelligence, 2016
Abstract Cross-validation of soft computing techniques needs to be done efficiently to avoid over... more Abstract Cross-validation of soft computing techniques needs to be done efficiently to avoid overfitting and underfitting. This is more important in petroleum reservoir characterisation applications where the often-limited training and testing data subsets represent Wells with known and unknown target properties, respectively. Existing data stratification strategies have been haphazardly chosen without any experimental basis. In this study, the optimal training–testing stratification proportions have been rigorously investigated using the prediction of porosity and permeability of petroleum reservoirs as an experimental case. The comparative performances of seven traditional and advanced machine learning techniques were considered. The overall results suggested a recommendable optimum training stratification that could serve as a good reference for researchers in similar applications.
Abstract. Transparency in transactions can only be achieved through a controlled coordination of ... more Abstract. Transparency in transactions can only be achieved through a controlled coordination of the real and cyber worlds. The success of Ubiquitous Commerce Systems (UCS) relies on the convergence of both worlds. The mobile devices are ubiquitous; they can be used anytime and anywhere. This poses a lot of security challenges in a ubiquitous society where business transactions will be involved. With the emergence of electronic payment and other business transaction solutions, ubiquity has the potential to make commerce freer and the transaction flows easier. However, privacy, security and mutual trust are critical to its development and use. Hence, for users of UCS services to have full confidence in the system, most especially while operating in a cashless society, absolute security must be put in place by stakeholders. This paper presents a Transactions Authorization Protocol (TAP) for the UCS. TAP is conceptualized to enhance security for UCS users. This protocol is less intrusi...
Journal of Petroleum Exploration and Production Technology, 2016
Computational intelligence (CI) techniques have positively impacted the petroleum reservoir chara... more Computational intelligence (CI) techniques have positively impacted the petroleum reservoir characterization and modeling landscape. However, studies have showed that each CI technique has its strengths and weaknesses. Some of the techniques have the ability to handle datasets of high dimensionality and fast in execution, while others are limited in their ability to handle uncertainties, difficult to learn, and could not deal with datasets of high or low dimensionality. The ''no free lunch'' theorem also gives credence to this problem as it postulates that no technique or method can be applicable to all problems in all situations. A technique that worked well on a problem may not perform well in another problem domain just as a technique that was written off on one problem may be promising with another. There was the need for robust techniques that will make the best use of the strengths to overcome the weaknesses while producing the best results. The machine learning concepts of hybrid intelligent system (HIS) have been proposed to partly overcome this problem. In this review paper, the impact of HIS on the petroleum reservoir characterization process is enumerated, analyzed, and extensively discussed. It was concluded that HIS has huge potentials in the improvement of petroleum reservoir property predictions resulting in improved exploration, more efficient exploitation, increased production, and more effective management of energy resources. Lastly, a number of yet-to-be-explored hybrid possibilities were recommended.
Journal of Petroleum Science and Engineering, 2017
SPE Middle East Oil and Gas Show and Conference, 2013
Accurate prediction of permeability remains the key to the determination of oil and gas reservoir... more Accurate prediction of permeability remains the key to the determination of oil and gas reservoir quality. A number of studies have been carried out to investigate the predictability of reservoir permeability from log measurements. More recent studies have attempted to predict permeability from seismic signals. Both log measurements and seismic signals have shown to provide rich information about the structure and texture of the subsurface and hence have jointly proven to be good predictors of permeability. However, previous studies on this subject were limited to the application of Artificial Neural Networks (ANN). With the persistent quest for more accurate predictions for more successful exploration and improved production, this paper investigates the effect of combining both seismic and log datasets with the application of more advanced Artificial Intelligence techniques on the accuracy of reservoir permeability predictions. Log measurements and seismic signals obtained from sev...
![Research paper thumbnail of Corrigendum to "Hybrid computational models for the characterization of oil and gas reservoirs" [Expert Systems with Applications 37 (2010) 5353-5363]](https://attachments.academia-assets.com/72019555/thumbnails/1.jpg)
](Expert Systems With Applications, 2011
A comparative study of the application of Gaussian Mixture Model (GMM) and Radial Basis Function ... more A comparative study of the application of Gaussian Mixture Model (GMM) and Radial Basis Function (RBF) in biometric recognition of voice has been carried out and presented. The application of machine learning techniques to biometric authentication and recognition problems has gained a widespread acceptance. In this research, a GMM model was trained, using Expectation Maximization (EM) algorithm, on a dataset containing 10 classes of vowels and the model was used to predict the appropriate classes using a validation dataset. For experimental validity, the model was compared to the performance of two different versions of RBF model using the same learning and validation datasets. The results showed very close recognition accuracy between the GMM and the standard RBF model, but with GMM performing better than the standard RBF by less than 1% and the two models outperformed similar models reported in literature. The DTREG version of RBF outperformed the other two models by producing 94.8% recognition accuracy. In terms of recognition time, the standard RBF was found to be the fastest among the three models.
Journal of Petroleum Science and Engineering, 2016
Artificial Intelligence techniques have been used in petroleum engineering to predict various res... more Artificial Intelligence techniques have been used in petroleum engineering to predict various reservoir properties such as porosity, permeability, water saturation, lithofacie and wellbore stability. The most extensively used of these techniques is Artificial Neural Networks (ANN). More recent techniques such as Support Vector Machines (SVM) have featured in the literature with better performance indices. However, SVM has not been widely embraced in petroleum engineering as a possibly better alternative to ANN. ANN has been reported to have a lot of limitations such as its lack of global optima. On the other hand, SVM has been introduced as a generalization of the Tikhonov Regularization procedure that ensures its global optima and offers ease of training. This paper presents a comparative study of the application of ANN and SVM models in the prediction of porosity and permeability of oil and gas reservoirs with carbonate platforms. Six datasets obtained from oil and gas reservoirs in two different geographical locations were used for the training, testing and validation of the models using the stratified sampling approach rather than the conventional static method of data division. The results showed that the SVM model performed better than the popularly used Feed forward Back propagation ANN with higher correlation coefficients and lower root mean squared errors. The SVM was also faster in terms of execution time. Hence, this work presents SVM as a possible alternative to ANN, especially, in the characterization of oil and gas reservoir properties. The new SVM model will assist petroleum exploration engineers to estimate various reservoir properties with better accuracy, leading to reduced exploration time and increased production.
79th EAGE Conference and Exhibition 2017 - Workshops, 2017
An approach, comprising statistical and artificial intelligence techniques, to modeling rock ceme... more An approach, comprising statistical and artificial intelligence techniques, to modeling rock cementation factor in a Saudi Arabian carbonate reservoir using wireline logs is presented. The objective is to obtain a more accurate prediction of rock cementation factor, denoted by the exponent, m, in Archie’s equation, as a variable log using multivariate linear regression (MLR), artificial neural networks, and support vector machines. Published equations by Nugent, Lucia and Shell are empirical derivations based on porosity logs and assumptions that may not be applicable in other geological settings. Typically, log analysts use the average of m values obtained from special core analysis (SCAL) measurements. Such constant values do not account for formation heterogeneity resulting in inaccurate water saturation and pore volume estimation with high operational and economic costs. In this study, six wireline logs from seven wells were combined with their corresponding core measured m values to build and optimize the proposed models to predict the m values for new wells or uncored sections of existing wells. The predicted m values produced by the MLR model closely matched available m data from SCAL measurements. This study fulfills the pressing need for variable m as a more accurate input to water saturation models.
First EAGE Digitalization Conference and Exhibition, 2020
Summary Grain size is a key input to various reservoir models. The models require a continuous lo... more Summary Grain size is a key input to various reservoir models. The models require a continuous log of grain size. Core samples are usually not available over the entire reservoir section. The most accurate grain size measurement is obtained from sieve and laser particle size analyses. These methods are expensive. The conventional method, the visual core description, is time-consuming, subjective, and nonreproducible. Alternative methods include the use of empirical equations, nuclear magnetic resonance (NMR) relaxation time, and acoustic velocities. These latter methods require inputs that are not sufficiently available, not applicable to different geological settings, or not available for all wells. This paper proposes a new methodology that estimates reservoir rock grain size for a new well or reservoir section from archival core description data and their corresponding wireline logs using machine learning technology. Nine wells from a clastic reservoir are used. Seven wells are combined to build the training set while the remaining two are used for model validation. Three machine learning methods are implemented and trained with optimized parameters. The results showed that, despite the subjectivity and bias associated with the core description data, the machine learning methods are capable of estimating the grain size for the validation wells.
Day 2 Tue, November 01, 2022
Sonic logs are important for deriving elastic moduli of rocks, which can be useful in calculating... more Sonic logs are important for deriving elastic moduli of rocks, which can be useful in calculating in-situ stresses, estimating safe drilling mud weight, controlling wellbore stability, and constructing velocity models for seismic processing. Practically, determining the geomechanical information of the subsurface in real-time can alleviate operational risks and improve formation evaluation. Since sonic logs are not acquired in real-time, machine learning can be utilized to estimate them in real-time using drilling parameters and mud gas data. This study uses Random Forest machine learning technique to predict compressional wave slowness in real-time by utilizing surface drilling parameters and mud gas data. Out of a total of five wells, the regression model is trained with data from four wells. The input parameters for each depth point include conventional drilling parameters (rate of penetration, torque, weight on bit, etc.) and mud gas data. Various preprocessing techniques were a...
Journal of Petroleum Exploration and Production Technology, 2021
Seismic velocity modeling is a crucial step in seismic processing that enables the use of velocit... more Seismic velocity modeling is a crucial step in seismic processing that enables the use of velocity information from both seismic and wells to map the depth and thickness of subsurface layers interpreted from seismic images. The velocity can be obtained in the form of normal moveout (NMO) velocity or by an inversion (optimization) process such as in full-waveform inversion (FWI). These methods have several limitations. These limitations include enormous time consumption in the case of NMO due to manual and heavy human involvement in the picking. As an optimization problem, it incurs high cost and suffers from nonlinearity issues. Researchers have proposed various machine learning (ML) techniques including unsupervised, supervised, and semi-supervised learning methods to model the velocity more efficiently. The focus of the studies is mostly to automate the NMO velocity picking, improve the convergence in FWI, and apply FWI using ML directly from the data. In the purview of the digita...
This paper presents a comparative study of the performance of three versions of Adaptive Neuro-Fu... more This paper presents a comparative study of the performance of three versions of Adaptive Neuro-Fuzzy Inference System (ANFIS) hybrid model and two innovative hybrid models in the prediction of oil and gas reservoir properties. ANFIS is a hybrid learning algorithm that combines the rule-based inferencing of fuzzy logic and the back-propagation learning procedure of Artificial Neural Networks. Functional Networks-Support Vector Machines (FN-SVM) and Functional Networks-Type-2 Fuzzy Logic (FN-T2FL) were proposed to improve the performance of the stand-alone SVM and T2FL models respectively. The FN component of the FN-T2FL hybrid model automatically extracts the most relevant attributes from the input data using the least square fitting algorithm as an improvement over the individual Functional Networks and Type-2 Fuzzy Logic models. The former is more promising as it combines two existing techniques that are very close in performance and well known for their computational stability and fast processing. The FN-SVM hybrid model also benefits from the excellent performance of the least-square-based model-selection algorithm of Functional Networks and the non-linear high-dimensional feature transformation capability that is based on structural risk minimization and Tikhonov regularization properties of SVM. Training and testing the SVM component of the hybrid model with the best and dimensionally-reduced variables from the input data resulted in better performance with higher correlation coefficients, lower root mean square errors and less execution time than the traditional SVM model. A comparison of FN-SVM and FN-T2FL with the three versions of ANFIS showed the superiority of the FN-SVM model over the others. The three ANFIS models still proved to be good in solving real industrial problems due to their speed of execution especially in dense data conditions.
SPE Middle East Oil and Gas Show and Conference, 2013
ABSTRACT This paper presents a comparative study of the capabilities of Extreme Learning Machines... more ABSTRACT This paper presents a comparative study of the capabilities of Extreme Learning Machines (ELM), Decision Trees (DT) and Artificial Neural Networks (ANN), in the prediction of permeability from specific surface area, porosity and water saturation. ANN has been applied in the prediction of various oil and gas properties but with limitations such as computational instability due to its lack of global optima. ELM and DT are recent advances in Artificial Intelligence with improved architectures and better performance. The techniques were optimized and applied to the same carbonate reservoir field dataset . Following the popular convention and to ensure fairness, a stratified sampling approach was used to randomly extract 70% of the dataset for training while the remaining 30% was used for testing. The results showed that ELM performed best with the highest correlation coefficient, lowest root mean square error and shortest execution time. This agrees perfectly with the literature that ELM has a more compact architecture optimized for faster execution than the original ANN. DT was also found to be a promising technique for reservoir modeling. The results showed that ELM performed best with the highest correlation coefficient, lowest root mean square error and shortest execution time. This agrees perfectly with the literature that ELM has a more compact architecture optimized for faster execution than the original ANN. DT was also found to be a promising technique for reservoir modeling.
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 2015
System availability is a key performance measure in the process industry. It ensures continuous o... more System availability is a key performance measure in the process industry. It ensures continuous operation of facilities to meet production targets, personnel safety and environmental sustainability. Process machinery condition assessment, early fault detection and its management are vital elements to ensure overall system availability. These elements can be explored and managed effectively by extracting hidden knowledge from machinery vibration information to improve plant availability and safe operations. This article describes a decision tree–based computational intelligence model using machinery vibration data to detect machinery faults, their severity, and suggests appropriate action to avoid unscheduled failures. Vibration data for this work were collected using a machinery simulator and real-world machine to show the applicability of the proposed model. Later, the data were analyzed to detect faults using decision tree–based model that was developed in MATLAB. Fault detection classification accuracies of 98% during training and 93% during testing showed excellent performance of the proposed model. The model also revealed that the proposed formulation has capability of detecting faults correctly in the range of 98%−99%. The results showed that the proposed decision tree–based model is effective in evaluating the condition of process machinery and predicting unscheduled equipment breakdowns with better accuracy and with reduced human effort.
Expert Systems with Applications, 2010
ABSTRACT The process of combining multiple computational intelligence techniques to build a singl... more ABSTRACT The process of combining multiple computational intelligence techniques to build a single hybrid model has become increasingly popular. As reported in the literature, the performance indices of these hybrid models have proved to be better than the individual components when used alone. Hybrid models are extremely useful for reservoir characterization in petroleum engineering, which requires high-accuracy predictions for efficient exploration and management of oil and gas resources.In this paper, we have utilized the capabilities of data mining and computational intelligence in the prediction of porosity and permeability, two important petroleum reservoir characteristics, based on the hybridization of Fuzzy Logic, Support Vector Machines, and Functional Networks, using several real-life well-logs. Two hybrid models have been built. In both, Functional Networks were used to select the best of the predictor variables for training directly from input data by using its functional approximation capability with least square fitting algorithm. In the first model (FFS), the selected predictor variables were passed to Type-2 Fuzzy Logic System to handle uncertainties and extract inference rules, while Support Vector Machines made the final predictions. In the second, the selected predictor variables were passed to Support Vector Machines for training by transforming them to a higher dimensional space, and then to Type-2 Fuzzy Logic to handle uncertainties, extract inference rules and make final predictions.The simulation results show that the hybrid models perform better than the individual techniques when used alone for the same datasets with their higher correlation coefficients. In terms of execution time, the hybrid models took less time for both training and testing than the Type-2 Fuzzy Logic, but more time than Functional Networks and Support Vector Machines. This could be the price for having a better and more robust model. The hybrid models also performed better than a combination of two of the individual components, Type-2 Fuzzy Logic and Support Vector Machines, in terms of higher correlation coefficients as well as lower execution times. This is due to the effective role of Functional Networks, as a best-variable selector in the hybrid models.
Journal of Experimental & Theoretical Artificial Intelligence, 2016
Abstract Cross-validation of soft computing techniques needs to be done efficiently to avoid over... more Abstract Cross-validation of soft computing techniques needs to be done efficiently to avoid overfitting and underfitting. This is more important in petroleum reservoir characterisation applications where the often-limited training and testing data subsets represent Wells with known and unknown target properties, respectively. Existing data stratification strategies have been haphazardly chosen without any experimental basis. In this study, the optimal training–testing stratification proportions have been rigorously investigated using the prediction of porosity and permeability of petroleum reservoirs as an experimental case. The comparative performances of seven traditional and advanced machine learning techniques were considered. The overall results suggested a recommendable optimum training stratification that could serve as a good reference for researchers in similar applications.
Abstract. Transparency in transactions can only be achieved through a controlled coordination of ... more Abstract. Transparency in transactions can only be achieved through a controlled coordination of the real and cyber worlds. The success of Ubiquitous Commerce Systems (UCS) relies on the convergence of both worlds. The mobile devices are ubiquitous; they can be used anytime and anywhere. This poses a lot of security challenges in a ubiquitous society where business transactions will be involved. With the emergence of electronic payment and other business transaction solutions, ubiquity has the potential to make commerce freer and the transaction flows easier. However, privacy, security and mutual trust are critical to its development and use. Hence, for users of UCS services to have full confidence in the system, most especially while operating in a cashless society, absolute security must be put in place by stakeholders. This paper presents a Transactions Authorization Protocol (TAP) for the UCS. TAP is conceptualized to enhance security for UCS users. This protocol is less intrusi...
Journal of Petroleum Exploration and Production Technology, 2016
Computational intelligence (CI) techniques have positively impacted the petroleum reservoir chara... more Computational intelligence (CI) techniques have positively impacted the petroleum reservoir characterization and modeling landscape. However, studies have showed that each CI technique has its strengths and weaknesses. Some of the techniques have the ability to handle datasets of high dimensionality and fast in execution, while others are limited in their ability to handle uncertainties, difficult to learn, and could not deal with datasets of high or low dimensionality. The ''no free lunch'' theorem also gives credence to this problem as it postulates that no technique or method can be applicable to all problems in all situations. A technique that worked well on a problem may not perform well in another problem domain just as a technique that was written off on one problem may be promising with another. There was the need for robust techniques that will make the best use of the strengths to overcome the weaknesses while producing the best results. The machine learning concepts of hybrid intelligent system (HIS) have been proposed to partly overcome this problem. In this review paper, the impact of HIS on the petroleum reservoir characterization process is enumerated, analyzed, and extensively discussed. It was concluded that HIS has huge potentials in the improvement of petroleum reservoir property predictions resulting in improved exploration, more efficient exploitation, increased production, and more effective management of energy resources. Lastly, a number of yet-to-be-explored hybrid possibilities were recommended.
Journal of Petroleum Science and Engineering, 2017
SPE Middle East Oil and Gas Show and Conference, 2013
Accurate prediction of permeability remains the key to the determination of oil and gas reservoir... more Accurate prediction of permeability remains the key to the determination of oil and gas reservoir quality. A number of studies have been carried out to investigate the predictability of reservoir permeability from log measurements. More recent studies have attempted to predict permeability from seismic signals. Both log measurements and seismic signals have shown to provide rich information about the structure and texture of the subsurface and hence have jointly proven to be good predictors of permeability. However, previous studies on this subject were limited to the application of Artificial Neural Networks (ANN). With the persistent quest for more accurate predictions for more successful exploration and improved production, this paper investigates the effect of combining both seismic and log datasets with the application of more advanced Artificial Intelligence techniques on the accuracy of reservoir permeability predictions. Log measurements and seismic signals obtained from sev...
Expert Systems With Applications, 2011
A comparative study of the application of Gaussian Mixture Model (GMM) and Radial Basis Function ... more A comparative study of the application of Gaussian Mixture Model (GMM) and Radial Basis Function (RBF) in biometric recognition of voice has been carried out and presented. The application of machine learning techniques to biometric authentication and recognition problems has gained a widespread acceptance. In this research, a GMM model was trained, using Expectation Maximization (EM) algorithm, on a dataset containing 10 classes of vowels and the model was used to predict the appropriate classes using a validation dataset. For experimental validity, the model was compared to the performance of two different versions of RBF model using the same learning and validation datasets. The results showed very close recognition accuracy between the GMM and the standard RBF model, but with GMM performing better than the standard RBF by less than 1% and the two models outperformed similar models reported in literature. The DTREG version of RBF outperformed the other two models by producing 94.8% recognition accuracy. In terms of recognition time, the standard RBF was found to be the fastest among the three models.
Journal of Petroleum Science and Engineering, 2016
Artificial Intelligence techniques have been used in petroleum engineering to predict various res... more Artificial Intelligence techniques have been used in petroleum engineering to predict various reservoir properties such as porosity, permeability, water saturation, lithofacie and wellbore stability. The most extensively used of these techniques is Artificial Neural Networks (ANN). More recent techniques such as Support Vector Machines (SVM) have featured in the literature with better performance indices. However, SVM has not been widely embraced in petroleum engineering as a possibly better alternative to ANN. ANN has been reported to have a lot of limitations such as its lack of global optima. On the other hand, SVM has been introduced as a generalization of the Tikhonov Regularization procedure that ensures its global optima and offers ease of training. This paper presents a comparative study of the application of ANN and SVM models in the prediction of porosity and permeability of oil and gas reservoirs with carbonate platforms. Six datasets obtained from oil and gas reservoirs in two different geographical locations were used for the training, testing and validation of the models using the stratified sampling approach rather than the conventional static method of data division. The results showed that the SVM model performed better than the popularly used Feed forward Back propagation ANN with higher correlation coefficients and lower root mean squared errors. The SVM was also faster in terms of execution time. Hence, this work presents SVM as a possible alternative to ANN, especially, in the characterization of oil and gas reservoir properties. The new SVM model will assist petroleum exploration engineers to estimate various reservoir properties with better accuracy, leading to reduced exploration time and increased production.