Evaluation of the Total Organic Carbon (TOC) Using Different Artificial Intelligence Techniques

Mahmoud, Ahmed Abdulhamid; Elkatatny, Salaheldin; Ali, Abdulwahab; Abouelresh, Mohamed O.; Abdulraheem, Abdulazeez

doi:10.3390/su11205643

Cited by 37 publications

(14 citation statements)

References 41 publications

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“…Training models require an increment of energy consumption due to the resource's utilization and operation calculations. The leading research in this topic focuses on evaluating energy consumption in terms of software (i.e., language programming libraries for AI) [103] and hardware levels (i.e., identifying critical circuit paths during AI operations) [104]. In our infrastructure, we focus on the software level in the computational service.…”

Section: Societal and Environmental Well-beingmentioning

confidence: 99%

A Predictive Analytics Infrastructure to Support a Trustworthy Early Warning System

et al. 2021

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Learning analytics is quickly evolving. Old fashioned dashboards with descriptive information and trends about what happened in the past are slightly substituted by new dashboards with forecasting information and predicting relevant outcomes about learning. Artificial intelligence is aiding this revolution. The accessibility to computational resources has increased, and specific tools and packages for integrating artificial intelligence techniques leverage such new analytical tools. However, it is crucial to develop trustworthy systems, especially in education where skepticism about their application is due to the risk of teachers’ replacement. However, artificial intelligence systems should be seen as companions to empower teachers during the teaching and learning process. During the past years, the Universitat Oberta de Catalunya has advanced developing a data mart where all data about learners and campus utilization are stored for research purposes. The extensive collection of these educational data has been used to build a trustworthy early warning system whose infrastructure is introduced in this paper. The infrastructure supports such a trustworthy system built with artificial intelligence procedures to detect at-risk learners early on in order to help them to pass the course. To assess the system’s trustworthiness, we carried out an evaluation on the basis of the seven requirements of the European Assessment List for trustworthy artificial intelligence (ALTAI) guidelines that recognize an artificial intelligence system as a trustworthy one. Results show that it is feasible to build a trustworthy system wherein all seven ALTAI requirements are considered at once from the very beginning during the design phase.

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Section: Societal and Environmental Well-beingmentioning

confidence: 99%

A Predictive Analytics Infrastructure to Support a Trustworthy Early Warning System

et al. 2021

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“…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

confidence: 99%

Application of Machine Learning in Evaluation of the Static Young’s Modulus for Sandstone Formations

2020

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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.

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“…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

confidence: 99%

Prediction of the Rate of Penetration while Drilling Horizontal Carbonate Reservoirs Using the Self-Adaptive Artificial Neural Networks Technique

et al. 2020

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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.

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Evaluation of the Total Organic Carbon (TOC) Using Different Artificial Intelligence Techniques

Cited by 37 publications

References 41 publications

A Predictive Analytics Infrastructure to Support a Trustworthy Early Warning System

A Predictive Analytics Infrastructure to Support a Trustworthy Early Warning System

Application of Machine Learning in Evaluation of the Static Young’s Modulus for Sandstone Formations

Prediction of the Rate of Penetration while Drilling Horizontal Carbonate Reservoirs Using the Self-Adaptive Artificial Neural Networks Technique

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