A New Scheme to Improve the Performance of Artificial Intelligence Techniques for Estimating Total Organic Carbon from Well Logs

Wang, Pan

doi:10.3390/en11040747

Cited by 16 publications

(5 citation statements)

References 30 publications

(39 reference statements)

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“… 39 Subsequently, conventional logs have been fed as inputs, that is, gamma ray log, density log, acoustic log, deep and medium resistivity logs, and porosity log in addition to uranium (U), thorium (Th), and potassium (K) contents. 42 − 44 X-ray fluorescence elements’ data (such as copper and nickel) and thermal neutron porosity 1 as well as conventional log combinations displayed correlation with the TOC in the investigated environment 45 − 50 and were proofing evidence of AI reliability in predicting TOC. Table 1 provides a summary of the studies conducted for predicting the organic matter in shale formations.…”

Section: Introductionmentioning

confidence: 73%

“…Artificial intelligence (AI) has been considerably used in the last few years in oil and gas research, and much work has been made on the prediction of TOC based on core and well log data. ,− In most cases, AI methods are not globally applicable due to the heterogeneity of shales, which indicates the cruciality of studying the nature of the targeted fields and picking proper logs for the model. , Huang et al demonstrated one of the early applications of AI in predicting TOC using only three conventional (gamma ray, resistivity, and sonic) logs as an input and a pseudo-TOC log calculated from an empirical correlation following the Passey et al approaches . Subsequently, conventional logs have been fed as inputs, that is, gamma ray log, density log, acoustic log, deep and medium resistivity logs, and porosity log in addition to uranium (U), thorium (Th), and potassium (K) contents. − X-ray fluorescence elements’ data (such as copper and nickel) and thermal neutron porosity as well as conventional log combinations displayed correlation with the TOC in the investigated environment − and were proofing evidence of AI reliability in predicting TOC. Table provides a summary of the studies conducted for predicting the organic matter in shale formations.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Total Organic Carbon in Organic-Rich Shale Rocks Using Thermal Neutron Parameters

et al. 2023

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Total organic carbon (TOC) content is one of the crucial parameters that determine the value of the source rock. The TOC content gives important indications about the source rocks and hydrocarbon volume. Various techniques have been utilized for TOC quantification, either by geochemical analysis of source rocks in laboratories or using well logs to develop mathematical correlations and advanced machine learning models. Laboratory methods require intense sampling intervals to have an accurate understanding of the reservoir, and depending on the thickness of the interested formation, it can be time-consuming and costly. Empirical correlations based on well logs (e.g., density, sonic, gamma ray, and resistivity) showed fast predictions and very reasonable accuracies. However, other important parameters such as thermal neutron logs have not been studied yet as a potential input for providing reliable TOC predictions. Also, different studies estimate the TOC based on the well-logging data for various formations; however, limited studies were reported to predict the TOC for the Horn River Formation. Therefore, the objective of this study is to estimate the TOC variations based on the thermal neutron logs using one of the largest source rocks in Canada: The Horn River Formation. More than 150 data sets were collected and used in this work. The parameters of the artificial neural network (ANN) model were fine-tuned in order to improve the model’s prediction performance. Furthermore, an empirical correlation was developed utilizing the optimized ANN model to allow fast and direct application for the developed model. The developed correlation can predict the TOC with an average absolute error of 0.52 wt %. The proposed TOC model was able to outperform the previous models, and the coefficient of determination was increased from 0.28 to 0.73. Overall, the proposed TOC model can provide high accuracy for TOC ranges from 0.3 to 6.44 wt %. The developed model can provide a real-time quantification for the organic matter maturity, helping to allocate the zones of mature organic matter within the drilled formations.

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Section: Introductionmentioning

confidence: 73%

Section: Introductionmentioning

confidence: 99%

Prediction of Total Organic Carbon in Organic-Rich Shale Rocks Using Thermal Neutron Parameters

et al. 2023

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“…The basic idea of LSSVR is to use the known sample data to obtain a best fitting function [ 37 ] and then input new sample data according to this function to calculate the corresponding output value. Given a training sample set,

where x i ∈ R n , y i ∈ Y = R , i =1,2,…, l .…”

Section: Theory and Methodologymentioning

confidence: 99%

Modeling and Estimation of CO2 Emissions in China Based on Artificial Intelligence

Wang

Zhong

Yao

2022

Computational Intelligence and Neuroscience

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Since China’s reform and opening up, the social economy has achieved rapid development, followed by a sharp increase in carbon dioxide (CO2) emissions. Therefore, at the 75th United Nations General Assembly, China proposed to achieve carbon peaking by 2030 and carbon neutrality by 2060. The research work on advance forecasting of CO2 emissions is essential to achieve the above-mentioned carbon peaking and carbon neutrality goals in China. In order to achieve accurate prediction of CO2 emissions, this study establishes a hybrid intelligent algorithm model suitable for CO2 emissions prediction based on China’s CO2 emissions and related socioeconomic indicator data from 1971 to 2017. The hyperparameters of Least Squares Support Vector Regression (LSSVR) are optimized by the Adaptive Artificial Bee Colony (AABC) algorithm to build a high-performance hybrid intelligence model. The research results show that the hybrid intelligent algorithm model designed in this paper has stronger robustness and accuracy with relative error almost within ±5% in the advance prediction of CO2 emissions. The modeling scheme proposed in this study can not only provide strong support for the Chinese government and industry departments to formulate policies related to the carbon peaking and carbon neutrality goals, but also can be extended to the research of other socioeconomic-related issues.

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“…In turn, high maturity for the generation of hydrocarbons level is achieved by the lower parts of the Silurian, Ordovician, and Cambrian deposits [26][27][28]. The results presented in this article, obtained through methods using artificial intelligence, may be a valuable supplement to these results [29].…”

Section: Introductionmentioning

confidence: 95%

Use of Cluster Analysis to Group Organic Shale Gas Rocks by Hydrocarbon Generation Zones

2022

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In the last decade, exploration for unconventional hydrocarbon (shale gas) reservoirs has been carried out in Poland. The drilling of wells in prospective shale gas areas supplies numerous physicochemical measurements from rock and reservoir fluid samples. The objective of this paper is to present the method that has been developed for finding similarities between individual geological structures in terms of their hydrocarbon generation properties and hydrocarbon resources. The measurements and geochemical investigations of six wells located in the Ordovician, Silurian, and Cambrian formations of the Polish part of the East European Platform are used. Cluster analysis is used to compare and classify objects described by multiple attributes. The focus is on the issue of generating clusters that group samples within the gas, condensate, and oil windows. The vitrinite reflectance value (Ro) is adopted as the criterion for classifying individual samples into the respective windows. An additional issue was determining other characteristic geochemical properties of the samples classified into the selected clusters. Two variants of cluster analysis are applied—the furthest neighbor method and Ward’s method—which resulted in 10 and 11 clusters, respectively. Particular attention was paid to the mean Ro values (within each cluster), allowing the classification of samples from a given cluster into one of the windows (gas, condensate, or oil). Using these methods, the samples were effectively classified into individual windows, and their percentage share within the Silurian, Ordovician, and Cambrian units is determined.

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A New Scheme to Improve the Performance of Artificial Intelligence Techniques for Estimating Total Organic Carbon from Well Logs

Cited by 16 publications

References 30 publications

Prediction of Total Organic Carbon in Organic-Rich Shale Rocks Using Thermal Neutron Parameters

Prediction of Total Organic Carbon in Organic-Rich Shale Rocks Using Thermal Neutron Parameters

Modeling and Estimation of CO2 Emissions in China Based on Artificial Intelligence

Use of Cluster Analysis to Group Organic Shale Gas Rocks by Hydrocarbon Generation Zones

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