A new methodology for identification of potential pay zones from well logs: Intelligent system establishment and application in the Eastern Junggar Basin, China

Guo, Dali; Zhu, Kai; Wang, Liang; Li, Jiaqi; Xu, Jia

doi:10.1007/s12182-014-0338-1

Cited by 11 publications

(3 citation statements)

References 19 publications

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“…The next interesting neural network application -for identification of hydrocarbons layers on the basis of well log interpretation -is described in the paper (Dali Guo et al, 2014). As well log interpretation is one of the prime sources of information for deep lithology in drilling research, such an application of a neural model is especially interesting.…”

Section: Identification Of Hydrocarbons Layersmentioning

confidence: 99%

Neural Networks In Mining Sciences – General Overview And Some Representative Examples

Tadeusiewicz¹

2015

Archives of Mining Sciences

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The many difficult problems that must now be addressed in mining sciences make us search for ever newer and more efficient computer tools that can be used to solve those problems. Among the numerous tools of this type, there are neural networks presented in this article -which, although not yet widely used in mining sciences, are certainly worth consideration. Neural networks are a technique which belongs to so called artificial intelligence, and originates from the attempts to model the structure and functioning of biological nervous systems. Initially constructed and tested exclusively out of scientific curiosity, as computer models of parts of the human brain, neural networks have become a surprisingly effective calculation tool in many areas: in technology, medicine, economics, and even social sciences. Unfortunately, they are relatively rarely used in mining sciences and mining technology. The article is intended to convince the readers that neural networks can be very useful also in mining sciences. It contains information how modern neural networks are built, how they operate and how one can use them. The preliminary discussion presented in this paper can help the reader gain an opinion whether this is a tool with handy properties, useful for him, and what it might come in useful for.Of course, the brief introduction to neural networks contained in this paper will not be enough for the readers who get convinced by the arguments contained here, and want to use neural networks. They will still need a considerable portion of detailed knowledge so that they can begin to independently create and build such networks, and use them in practice. However, an interested reader who decides to try out the capabilities of neural networks will also find here links to references that will allow him to start exploration of neural networks fast, and then work with this handy tool efficiently. This will be easy, because there are currently quite a few ready-made computer programs, easily available, which allow their user to quickly and effortlessly create artificial neural networks, run them, train and use in practice.The key issue is the question how to use these networks in mining sciences. The fact that this is possible and desirable is shown by convincing examples included in the second part of this study. From the very rich literature on the various applications of neural networks, we have selected several works that show how and what neural networks are used in the mining industry, and what has been achieved thanks to their use. The review of applications will continue in the next article, filed already for publication

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Section: Identification Of Hydrocarbons Layersmentioning

confidence: 99%

Neural Networks In Mining Sciences – General Overview And Some Representative Examples

Tadeusiewicz¹

2015

Archives of Mining Sciences

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“…Experiments show that LightGBM and CatBoost are the preferred algorithms for lithology classification by using well logging data. A large number of similar studies can be seen in [ 12 , 13 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Well Logging Based Lithology Identification Model Establishment Under Data Drift: A Transfer Learning Method

Liu

Cao

et al. 2020

Sensors

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Recent years have witnessed the development of the applications of machine learning technologies to well logging-based lithology identification. Most of the existing work assumes that the well loggings gathered from different wells share the same probability distribution; however, the variations in sedimentary environment and well-logging technique might cause the data drift problem; i.e., data of different wells have different probability distributions. Therefore, the model trained on old wells does not perform well in predicting the lithologies in newly-coming wells, which motivates us to propose a transfer learning method named the data drift joint adaptation extreme learning machine (DDJA-ELM) to increase the accuracy of the old model applying to new wells. In such a method, three key points, i.e., the project mean maximum mean discrepancy, joint distribution domain adaptation, and manifold regularization, are incorporated into extreme learning machine. As found experimentally in multiple wells in Jiyang Depression, Bohai Bay Basin, DDJA-ELM could significantly increase the accuracy of an old model when identifying the lithologies in new wells.

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“…Schlanser et al [7] tested a statistical clustering algorithm with geophysical logs for lithofacies classification in the Marcellus Shale. Guo et al [8] proposed a combination model--three ANNs to predict porosity, permeability and shale content respectively following a neuro-fuzzy inference machine--for pay zones recognition. Many other similar studies can be seen in [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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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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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A new methodology for identification of potential pay zones from well logs: Intelligent system establishment and application in the Eastern Junggar Basin, China

Cited by 11 publications

References 19 publications

Neural Networks In Mining Sciences – General Overview And Some Representative Examples

Neural Networks In Mining Sciences – General Overview And Some Representative Examples

Well Logging Based Lithology Identification Model Establishment Under Data Drift: A Transfer Learning Method

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

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