Abstract:Total organic carbon (TOC) is the amount of carbon present in an organic compound and is often used as an essential factor for unconventional shale resources evaluation. The previous models for TOC determination were either based on density log data only and considered the presence of organic matter is proportional to the bulk density, or based on resistivity log, sonic or density logs as well as the formation level of maturity (LOM), where these models assumed a linear relation between resistivity and porosit… Show more
“…Machine learning techniques are used in several scientific and engineering fields since the early 1990s to solve complicated non-linear problems. Petroleum engineers and petroleum geologists use different machine learning techniques to solve problems related to petroleum industry, such as the characterization of the heterogeneous hydrocarbon reservoirs [33,34], evaluation of the reserve of unconventional reservoirs [35][36][37][38], estimation of the rock mechanical parameters, such as the static Poisson's ratio in carbonate reservoirs [39] and the static Young's modulus for sandstone reservoirs [24,40], evaluation of the integrity of wellbore casing [41,42], optimization of drilling hydraulics [43], evaluation of pore pressure and fracture pressure [44,45], hydrocarbon recovery factor estimation [46,47], determination of the alteration in the drilling fluids rheology in real-time [48,49], optimization of rate of penetration [50,51], prediction of the formation tops [52], and others.…”
Section: Applications Of Machine Learning In Petroleum Engineeringmentioning
Prediction of the mechanical characteristics of the reservoir formations, such as static Young’s modulus (Estatic), is very important for the evaluation of the wellbore stability and development of the earth geomechanical model. Estatic considerably varies with the change in the lithology. Therefore, a robust model for Estatic prediction is needed. In this study, the predictability of Estatic for sandstone formation using four machine learning models was evaluated. The design parameters of the machine learning models were optimized to improve their predictability. The machine learning models were trained to estimate Estatic based on bulk formation density, compressional transit time, and shear transit time. The machine learning models were trained and tested using 592 well log data points and their corresponding core-derived Estatic values collected from one sandstone formation in well-A and then validated on 38 data points collected from a sandstone formation in well-B. Among the machine learning models developed in this work, Mamdani fuzzy interference system was the highly accurate model to predict Estatic for the validation data with an average absolute percentage error of only 1.56% and R of 0.999. The developed static Young’s modulus prediction models could help the new generation to characterize the formation rock with less cost and safe operation.
“…Machine learning techniques are used in several scientific and engineering fields since the early 1990s to solve complicated non-linear problems. Petroleum engineers and petroleum geologists use different machine learning techniques to solve problems related to petroleum industry, such as the characterization of the heterogeneous hydrocarbon reservoirs [33,34], evaluation of the reserve of unconventional reservoirs [35][36][37][38], estimation of the rock mechanical parameters, such as the static Poisson's ratio in carbonate reservoirs [39] and the static Young's modulus for sandstone reservoirs [24,40], evaluation of the integrity of wellbore casing [41,42], optimization of drilling hydraulics [43], evaluation of pore pressure and fracture pressure [44,45], hydrocarbon recovery factor estimation [46,47], determination of the alteration in the drilling fluids rheology in real-time [48,49], optimization of rate of penetration [50,51], prediction of the formation tops [52], and others.…”
Section: Applications Of Machine Learning In Petroleum Engineeringmentioning
Prediction of the mechanical characteristics of the reservoir formations, such as static Young’s modulus (Estatic), is very important for the evaluation of the wellbore stability and development of the earth geomechanical model. Estatic considerably varies with the change in the lithology. Therefore, a robust model for Estatic prediction is needed. In this study, the predictability of Estatic for sandstone formation using four machine learning models was evaluated. The design parameters of the machine learning models were optimized to improve their predictability. The machine learning models were trained to estimate Estatic based on bulk formation density, compressional transit time, and shear transit time. The machine learning models were trained and tested using 592 well log data points and their corresponding core-derived Estatic values collected from one sandstone formation in well-A and then validated on 38 data points collected from a sandstone formation in well-B. Among the machine learning models developed in this work, Mamdani fuzzy interference system was the highly accurate model to predict Estatic for the validation data with an average absolute percentage error of only 1.56% and R of 0.999. The developed static Young’s modulus prediction models could help the new generation to characterize the formation rock with less cost and safe operation.
“…AI techniques are used extensively in applications related to different engineering and scientific research areas [15][16][17][18][19][20], including in the petroleum industry where they can solve complicated problems such as prediction of drill bit wear from drilling parameters [21], real-time predictions of alterations in drilling fluid rheology [22,23], lithology identification [24], prediction of total organic carbon for the evaluation of unconventional resources [25][26][27][28][29], estimation of the oil recovery factor [30,31], estimation of pore and fracture pressures [32,33], evaluation of the static Young's modulus [34][35][36], estimation of the reservoir porosity [37], evaluation of the bubble point pressure [38], and the prediction of formation tops [39].…”
Section: Application Of Artificial Intelligence For Rate Of Penetratimentioning
Rate of penetration (ROP) is one of the most important drilling parameters for optimizing the cost of drilling hydrocarbon wells. In this study, a new empirical correlation based on an optimized artificial neural network (ANN) model was developed to predict ROP alongside horizontal drilling of carbonate reservoirs as a function of drilling parameters, such as rotation speed, torque, and weight-on-bit, combined with conventional well logs, including gamma-ray, deep resistivity, and formation bulk density. The ANN model was trained using 3000 data points collected from Well-A and optimized using the self-adaptive differential evolution (SaDE) algorithm. The optimized ANN model predicted ROP for the training dataset with an average absolute percentage error (AAPE) of 5.12% and a correlation coefficient (R) of 0.960. A new empirical correlation for ROP was developed based on the weights and biases of the optimized ANN model. The developed correlation was tested on another dataset collected from Well-A, where it predicted ROP with AAPE and R values of 5.80% and 0.951, respectively. The developed correlation was then validated using unseen data collected from Well-B, where it predicted ROP with an AAPE of 5.29% and a high R of 0.956. The ANN-based correlation outperformed all previous correlations of ROP estimation that were developed based on linear regression, including a recent model developed by Osgouei that predicted the ROP for the validation data with a high AAPE of 14.60% and a low R of 0.629.
“…Applying any of the previously discussed correlations to evaluate TOC in formations different than the one developed leads to inaccurate predictions. Recently, Mahmoud et al [18,19] suggested an artificial neural network (ANN)-based correlation for TOC estimation in Barnett formation using conventional well logs. Later on, Elkatatny [20] applied the self-adaptive differential evolution algorithm to optimize Mahmoud et al's [18,19] ANN model and he was able to improve the model predictability.…”
Section: His Correlation In Equationmentioning
confidence: 99%
“…Since the early 1990s, AI techniques had been extensively applied in many scientific and engineering fields, including in the petroleum industry. Nowadays, AI has been used by petroleum engineers and geologists to solve problems related to unconventional hydrocarbon resources evaluation [18][19][20], reservoir characterization [21,22], bubble point pressure evaluation [23], prediction of real-time change in the rheological parameters of the drilling fluids [24,25], optimization of rate of penetration [26], estimation of rock mechanical parameters [27,28], prediction of pore pressure and fracture pressure [29,30], evaluation of the wellbore casing integrity [31,32], hydrocarbon recovery factor estimation [33,34] optimization of the drilling hydraulics [35], and others. AI techniques have also been applied successfully in other fields like social media [36,37].…”
Section: Different Applications Of Artificial Intelligence Techniquesmentioning
confidence: 99%
“…The AI model's validation was completed using unseen data collected from the Devonian shale formation. The total number of core derived TOC data collected from Devonian shale are 22 data points, out of these data, only 20,19,19, and 15 were found to fit within the range of the training data that is used to develop TSK-FIS, M-FIS, FNN, and SVM models, respectively. The range for the training data are summarized in Table 1.…”
Total organic carbon (TOC) is an essential parameter used in unconventional shale resources evaluation. Current methods that are used for TOC estimation are based, either on conducting time-consuming laboratory experiments, or on using empirical correlations developed for specific formations. In this study, four artificial intelligence (AI) models were developed to estimate the TOC using conventional well logs of deep resistivity, gamma-ray, sonic transit time, and bulk density. These models were developed based on the Takagi-Sugeno-Kang fuzzy interference system (TSK-FIS), Mamdani fuzzy interference system (M-FIS), functional neural network (FNN), and support vector machine (SVM). Over 800 data points of the conventional well logs and core data collected from Barnett shale were used to train and test the AI models. The optimized AI models were validated using unseen data from Devonian shale. The developed AI models showed accurate predictability of TOC in both Barnett and Devonian shale. FNN model overperformed others in estimating TOC for the validation data with average absolute percentage error (AAPE) and correlation coefficient (R) of 12.02%, and 0.879, respectively, followed by M-FIS and SVM, while TSK-FIS model showed the lowest predictability of TOC, with AAPE of 15.62% and R of 0.832. All AI models overperformed Wang models, which have recently developed to evaluate the TOC for Devonian formation.
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