Water Control Diagnostic Plot Pattern Recognition Using Support Vector Machine

Mukhanov, Akhan; Garcia, Carlos A.; Torres, Henry

doi:10.2118/191600-18rptc-ms

Cited by 4 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in Figures 12-14, the confusion matrix analysis is a method for judging the classification performance of neural network model, which shows the accuracy result of classification. Mukhanov et al [37] used the confusion matrix to evaluate the classification result of the waterlogging curve by the support vector machine technology. The confusion matrix separately calculates the number of misclassification classes and the number of correct classification classes in the model.…”

Section: Comparison Of Classification Performance For Fcnn and Cnnmentioning

confidence: 99%

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

Chu

Dong

et al. 2019

Energies

View full text Add to dashboard Cite

The precondition of well testing interpretation is to determine the appropriate well testing model. In numerous attempts in the past, automatic classification and identification of well testing plots have been limited to fully connected neural networks (FCNN). Compared with FCNN, the convolutional neural network (CNN) has a better performance in the domain of image recognition. Utilizing the newly proposed CNN, we develop a new automatic identification approach to evaluate the type of well testing curves. The field data in tight reservoirs such as the Ordos Basin exhibit various well test models. With those models, the corresponding well test curves are chosen as training samples. One-hot encoding, Xavier normal initialization, regularization technique, and Adam algorithm are combined to optimize the established model. The evaluation results show that the CNN has a better result when the ReLU function is used. For the learning rate and dropout rate, the optimized values respectively are 0.005 and 0.4. Meanwhile, when the number of training samples was greater than 2000, the performance of the established CNN tended to be stable. Compared with the FCNN of similar structure, the CNN is more suitable for classification of well testing plots. What is more, the practical application shows that the CNN can successfully classify 21 of the 25 cases.

show abstract

Section: Comparison Of Classification Performance For Fcnn and Cnnmentioning

confidence: 99%

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

Chu

Dong

et al. 2019

Energies

View full text Add to dashboard Cite

show abstract

The Role of Big Data Analytics in Exploration and Production: A Review of Benefits and Applications

Noshi

Assem

Schubert

2018

Day 1 Mon, December 10, 2018

View full text Add to dashboard Cite

Due to the decrease in commodity prices in a constantly dynamic environment, there has been a constant urge to maximize benefits and attain value from limited resources. Traditional empirical and numerical simulation techniques have failed to provide comprehensive optimized solutions in little time. Coupled with the immense volumes of data generated on a daily basis, a solution to tackle industry challenges became imminent. Various expert opinion fraught with bias has posed extra challenges to obtain timely cost-effective solutions. Data Analytics has provided substantial contributions in several sectors. However, its value has not been captured in the Oil and Gas industry. This paper presents a review of various Machine Learning applications in exploration, completions, production operations to date. An overview of data-driven workflows in the fields of electric submersible pump (ESP) failure and shutdown prediction, reservoir databases’ analysis, reduction of subsurface uncertainty, EOR decisions using scarce data, improved oil recovery estimation, production impact assessment, horizontal completion, fracturing techniques, production optimization in unconventional reservoirs, production management, and field surveillance, is presented. The review attempts to shed light on the benefits and applications of multiple challenges faced on a daily basis by scientists, field personnel, and engineers to help solve and optimize the industry's multi-faceted data-intense challenges.

show abstract

Application of Computer Vision in Machine Learning-Based Diagnosis of Water Production Mechanisms in Oil Wells

Abdelaziem¹,

Gawish

Farrag³

2023

SPE Journal

View full text Add to dashboard Cite

Summary Diagnostic plots, introduced by K. S. Chan, are widely used to determine excessive water production mechanisms. In this paper, we introduce a computer vision model that is capable of segmenting and identifying multiple Chan signatures per plot, for the sake of surveillance and early screening, given that wells could exhibit diverse mechanisms throughout their life cycle. As deep learning (DL) demands a vast amount of information, we start our workflow by building a data set of 10,000 publicly available oil wells that have experienced varying water production mechanisms. Next, we perform preprocessing and remove anomalies from production data, which could be deceptive in analysis. Then, we visualize Chan plots as images and annotate them. Thereafter, we split data set, carry out augmentation, and put data together to be used as input for a convolutional neural network (CNN) layer. Eventually, data are trained utilizing you only look once (YOLO)—a one-stage object detector, hyperparameters are tuned, and model performance is evaluated using mean average precision (mAP). The collected data from fields in Alaska and North Dakota represent oil wells that have been producing for decades. When working with wells that possess noisy production data, we recognized challenge, bias, and tedium in human interpretation of Chan plots. Subsequently, we observed the inevitability of cleaning well production data before constructing the plots and thoroughly revealed its effect on enhancing model potentiality to get a fair score. In addition, we concluded that following a systematic approach of active learning, a technique that allows user to analyze mistakes of algorithm predictions and label data accordingly, accomplished a significant boost in model performance, especially with underrepresented classes. The proposed CNN model, which uses automatic feature extraction and expresses data in detail, is presumed to be robust as it successfully predicted multiple mechanisms of excessive water production, with confidence scores higher than 80%, in wells that exhibit different production conditions such as horizontal trajectories, artificial lift, waterflooding, stimulation, and other well intervention events. In this work, we introduce a novel computer vision model, which combines image processing and DL techniques to identify multiple water production signatures that a well can undergo and eliminate subjectivity of human interpretation. This approach has the potential to be effective, as a part of workflow automation, in expeditious surveillance of large oil fields. Source code is available on GitHub for public use.

show abstract

Water Control Diagnostic Plot Pattern Recognition Using Support Vector Machine

Cited by 4 publications

References 5 publications

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

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

The Role of Big Data Analytics in Exploration and Production: A Review of Benefits and Applications

Application of Computer Vision in Machine Learning-Based Diagnosis of Water Production Mechanisms in Oil Wells

Contact Info

Product

Resources

About