Machine Learning Algorithms for Classification Geology Data from Well Logging

Unconventional reservoir classification suffers low accuracy because of the complex geophysical properties. With good performance and moderate cost, geophysical logging is considered to be of great potential as the compromise solution between seismic and core. Since the "sweet-spot" depending on the sands' properties are implied not in the logging "point" but "segment", 1D-CNN is supposed to be a better fit for reservoir classification: It doesn't pay too much attention to the logging signal sequence itself like LSTM, but focuses on feature extraction from the whole signals combination. Moreover, samples would have various sizes because of different layer thickness. While 1D-CNN requires all samples must be converted to a uniform size before input. 1D fully convolutional network (1D-FCN) can receive any size input of layer thickness without manually aligning it to the same size. For the first time in this paper, structures of the five networks including two fully connected networks (global mapping artificial neural network, GM-ANN and point-to-point mapping artificial neural network, PPM-ANN), two fully convolutional networks (1D fully convolutional network with decision-level fusion, 1D-FCN-DEF and 1D fully convolutional network with data-level fusion, 1D-FCN-DAF) and the common 1D convolutional neural network (1D-CNN) are compared and evaluated the suitability for processing logging data in detail. Results on tight gas in Ordos basin of China illustrated by receiver operating characteristics (ROC) curves show that 1D-FCN-DEF and 1D-FCN-DAF have achieved the higher area under the curve (AUC) with the values of 0.8889 and 0.9107 respectively in average comparing to 0.7515, 0.8006 and 0.8364 of GM-ANN, PPM-ANN and 1D-CNN. Case study proves that 1D-FCNs are more accurate than other networks. This paper provides a suitable new idea for logging interpretation and expands the application scope of DL in geophysical logging signals processing. INDEX TERMSUnconventional Reservoir Classification (URC), Geophysical logging signals, 1D fully convolutional network (1D-FCN), 1D convolutional neural network (1D-CNN)

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“…Many other similar studies can be seen in [9][10][11][12]. The main disadvantages of ML are as follow [13]:…”

Section: Introductionmentioning

confidence: 94%

A Comparative Study of Five Networks for Reservoir Classification Based on Geophysical Logging Signals

Zhu

Wang

et al. 2020

IEEE Access

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“…To counteract the increasing computational burden of ever-growing datasets on more traditional models, many fields have recently adopted various machine learning algorithms ( Sharma et al, 2011 ; Montavon et al, 2013 ; Meredig et al, 2014 ; Merembayev et al, 2018 ; Schütt et al, 2020 ). Specifically, artificial neural networks (ANNs) are superior to conventional model systems both in terms of speed and accuracy when dealing with complex systems such as those governing global financial markets or weather patterns ( Holmstrom, 2016 ; Ghoddusi et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

“…To counteract the increasing computational burden of ever-growing datasets on more traditional models, many different fields have recently adopted various machine learning algorithms [34][35][36][37][38] . Specifically, artificial neural networks (ANNs) have been shown to be superior to traditional model systems both in terms of speed and accuracy when dealing with complex systems such as those governing global financial markets or weather patterns 39,40 .…”

Section: Introductionmentioning

confidence: 99%

Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons

Oláh

Pedersen

Rowan

2022

eLife

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Understanding the activity of the mammalian brain requires an integrative knowledge of circuits at distinct scales, ranging from ion channel gating to circuit connectomics. Computational models are regularly employed to understand how multiple parameters contribute synergistically to circuit behavior. However, traditional models of anatomically and biophysically realistic neurons are computationally demanding, especially when scaled to model local circuits. To overcome this limitation, we trained several artificial neural network (ANN) architectures to model the activity of realistic multicompartmental cortical neurons. We identified an ANN architecture that accurately predicted subthreshold activity and action potential firing. The ANN could correctly generalize to previously unobserved synaptic input, including in models containing nonlinear dendritic properties. When scaled, processing times were orders of magnitude faster compared with traditional approaches, allowing for rapid parameter-space mapping in a circuit model of Rett syndrome. Thus, we present a novel ANN approach allowing for rapid, detailed network experiments using inexpensive and commonly available computational resources.

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“…There are several works regarding application of data analysis methods for mining areas [5,6]. The importance of lithofacies detection for uranium mining is discussed and investigated in [7,8] using machine learning algorithms to solve multilabel lithofacies classification. The in situ leaching of uranium requires a better understanding of the permeable and impermeable rock types.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Machine Learning Algorithms in Predicting Lithofacies: Case Studies from Norway and Kazakhstan

et al. 2021

Self Cite

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Defining distinctive areas of the physical properties of rocks plays an important role in reservoir evaluation and hydrocarbon production as core data are challenging to obtain from all wells. In this work, we study the evaluation of lithofacies values using the machine learning algorithms in the determination of classification from various well log data of Kazakhstan and Norway. We also use the wavelet-transformed data in machine learning algorithms to identify geological properties from the well log data. Numerical results are presented for the multiple oil and gas reservoir data which contain more than 90 released wells from Norway and 10 wells from the Kazakhstan field. We have compared the the machine learning algorithms including KNN, Decision Tree, Random Forest, XGBoost, and LightGBM. The evaluation of the model score is conducted by using metrics such as accuracy, Hamming loss, and penalty matrix. In addition, the influence of the dataset features on the prediction is investigated using the machine learning algorithms. The result of research shows that the Random Forest model has the best score among considered algorithms. In addition, the results are consistent with outcome of the SHapley Additive exPlanations (SHAP) framework.

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Machine Learning Algorithms for Classification Geology Data from Well Logging

Cited by 19 publications

References 2 publications

A Comparative Study of Five Networks for Reservoir Classification Based on Geophysical Logging Signals

A Comparative Study of Five Networks for Reservoir Classification Based on Geophysical Logging Signals

Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons

A Comparison of Machine Learning Algorithms in Predicting Lithofacies: Case Studies from Norway and Kazakhstan

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