An Automatic Classification Method of Well Testing Plot Based on Convolutional Neural Network (CNN)

Chu, Hongyang; Li, Xinwei; Dong, Peng; Chen, Zhiming; Zhao, Xiaoliang; Zou, Jiandong

doi:10.3390/en12152846

Cited by 31 publications

(5 citation statements)

References 40 publications

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“…At present, none of these level III layers are in production and cannot be verified by actual production data. (6) The test quality classification method formed based on the gas-water ratio identification chart can distinguish the pros and cons of the test quality, and provide an effective standard for the inspection of the gas-water identification of the reservoir. However, the specific construction parameters and the operation of the equipment during the gas test also have a certain impact on the judgment of the gas test conclusion.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…However, due to the low abundance of tight sandstone oil and gas reservoirs, production has declined rapidly [4]. The current economic development is particularly difficult [5,6]. There are obvious differences between tight gas reservoirs and conventional gas reservoirs in the accumulation process, this reason directly leads to the generally high water saturation in tight oil and gas reservoirs [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research on Test and Logging Data Quality Classification for Gas–Water Identification

Xiang

Deng

et al. 2021

Energies

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Tight sandstone oil and gas reservoirs are widely distributed, rich in resources, with a bright prospect for exploration and development in China. Due to multiple evolutions of the structure and sedimentary system, the gas–water distribution laws are complicated in tight sandstone gas reservoirs in the northern Ordos area. It is difficult to identify gas and water layers in the study area. In addition, in the development and production, various factors, such as the failure of the instrument, the difference in construction parameters (injected sand volume, flowback rate), poor test results, and multi-layer joint testing lead to unreliable gas test results. Then, the inaccurate logging responses will be screened by unreliable gas test results for different types of fluids. It is hard to make high-precision fluid logging identification charts or models. Therefore, this article combines gas logging, well logging, testing and other data to research the test and logging data quality classification. Firstly, we select reliable standard samples through the initial gas test results. Secondly, we analyze the four main factors which affect the inaccuracy of gas test results. Thirdly, according to these factors, the flowback rate and the sand volume are determined as the main parameters. Then, we establish a recognition chart of injected sand volume/gas–water ratio. Finally, we proposed an evaluation method for testing quality classification. It provides a test basis for the subsequent identification of gas and water through the second logging interpretation. It also provides a theoretical basis for the exploration and evaluation of tight oil and gas reservoirs.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Test and Logging Data Quality Classification for Gas–Water Identification

Xiang

Deng

et al. 2021

Energies

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“…Convolutional neural networks (CNN) are commonly used in image classification models due to their high accuracy in supervised learning [ 27 ]. CNNs are preferred to fully connected neural networks (FCNN) for image classification because they do not utilize connection weights for pixels and instead use kernels, reducing computational complexity and number of required samples [ 28 , 29 ].…”

Section: Methodsmentioning

confidence: 99%

A Comparative Analysis of Deep Learning Models for Automated Cross-Preparation Diagnosis of Multi-Cell Liquid Pap Smear Images

et al. 2022

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Routine Pap smears can facilitate early detection of cervical cancer and improve patient outcomes. The objective of this work is to develop an automated, clinically viable deep neural network for the multi-class Bethesda System diagnosis of multi-cell images in Liquid Pap smear samples. 8 deep learning models were trained on a publicly available multi-class SurePath preparation dataset. This included the 5 best-performing transfer learning models, an ensemble, a novel convolutional neural network (CNN), and a CNN + autoencoder (AE). Additionally, each model was tested on a novel ThinPrep Pap dataset to determine model generalizability across different liquid Pap preparation methods with and without Deep CORAL domain adaptation. All models achieved accuracies >90% when classifying SurePath images. The AE CNN model, 99.80% smaller than the average transfer model, maintained an accuracy of 96.54%. During consecutive training attempts, individual transfer models had high variability in performance, whereas the CNN, AE CNN, and ensemble did not. ThinPrep Pap classification accuracies were notably lower but increased with domain adaptation, with ResNet101 achieving the highest accuracy at 92.65%. This indicates a potential area for future improvement: development of a globally relevant model that can function across different slide preparation methods.

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“…The recent advances in computational models and deep neural networks have provided promising results in different research fields 25 – 30 , including well-testing analysis 31 – 35 . Convolutional neural networks have many parameters due to vectorizing image input, which results in increasing the computational cost and training time.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic algorithm integrated neural networks for well-test analyses of petroleum reservoirs

Pandey

Aggarwal

Nath

et al. 2022

Sci Rep

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In recent years, well-test research has witnessed several works to automate reservoir model identification and characterization using computer-assisted models. Since the reservoir model identification is a classification problem, while its characterization is a regression-based task, their simultaneous accomplishment is always challenging. This work combines genetic algorithm optimization and artificial neural networks to identify and characterize homogeneous reservoir systems from well-testing data automatically. A total of eight prediction models, including two classifiers and six regressors, have been trained. The simulated well-test pressure derivatives with varying noise percentages comprise the training samples. The feature selection and hyperparameter tuning have been performed carefully using the genetic algorithm to enhance the prediction accuracy. The models were validated using nine simulated and one real-field test case. The optimized classifier identifies all the reservoir models with a classification accuracy higher than 79%. In addition, the statistical analysis approves that the optimized regressors accurately perform the reservoir characterization with mean relative errors of lower than 4.5%. The minimized manual interference reduces human bias, and the models have significant noise tolerance for practical applications.

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An Automatic Classification Method of Well Testing Plot Based on Convolutional Neural Network (CNN)

Cited by 31 publications

References 40 publications

Research on Test and Logging Data Quality Classification for Gas–Water Identification

Research on Test and Logging Data Quality Classification for Gas–Water Identification

A Comparative Analysis of Deep Learning Models for Automated Cross-Preparation Diagnosis of Multi-Cell Liquid Pap Smear Images

Metaheuristic algorithm integrated neural networks for well-test analyses of petroleum reservoirs

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