Machine Learning for Progressive Cavity Pump Performance Analysis: A Coal Seam Gas Case Study

Saghir, Fahd; Perdomo, M. E. Gonzalez; Behrenbruch, Peter

doi:10.15530/ap-urtec-2019-198281

Cited by 2 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some scholars thus have put forward some measures on PCP health management based on machine learning methods. For example, Saghir et al discussed how to use data collected from a data acquisition system to apply data approximation and unsupervised machine learning methods to time series datasets to help analyze PCP performance and detect abnormal pump behavior [4]. Hoday et al proposed a method based on abnormal monitoring to characterize PCP failures, maximize the information value of monitoring the operating conditions of each well, and minimize operating costs [5].…”

Section: Introductionmentioning

confidence: 99%

The Health Index Prediction Model and Application of PCP in CBM Wells Based on Deep Learning

et al. 2021

View full text Add to dashboard Cite

Aiming at the problems of the current production and operation status of the progressive cavity pump (PCP) in coalbed methane (CBM) wells which cannot be timely monitored, quantitatively evaluated, and accurately predicted, a five-step method for evaluating and predicting the health status of PCP wells is proposed: data preprocessing, principal parameter optimization, health index construction, health degree division, and health index prediction. Therein, a health index (HI) formulation was made based on deep learning, and a statistical method was used to define the health status of PCP wells as being healthy, subhealthy, or faulty. This allowed further research on the HI prediction model of PCP wells based on the long short-term memory (LSTM) network. As demonstrated in the study, they can reflect both the change trend and the contextual relevance of the health status of PCP wells with high accuracy to achieve real-time, quantitative, and accurate assessment and prediction. At the same time, the conclusion gives good guidance on the production performance analysis and failure warning of the PCP wells and suggests a new direction for the health status assessment and warning of other artificial lift equipment.

show abstract

Section: Introductionmentioning

confidence: 99%

The Health Index Prediction Model and Application of PCP in CBM Wells Based on Deep Learning

et al. 2021

View full text Add to dashboard Cite

show abstract

A Thorough Review of Machine Learning Applications in Oil and Gas Industry

Temizel

Canbaz

Palabiyik

et al. 2021

SPE/IATMI Asia Pacific Oil &Amp; Gas Conference and Exhibition

View full text Add to dashboard Cite

Reservoir engineering constitutes a major part of the studies regarding oil and gas exploration and production. Reservoir engineering has various duties, including conducting experiments, constructing appropriate models, characterization, and forecasting reservoir dynamics. However, traditional engineering approaches started to face challenges as the number of raw field data increases. It pushed the researchers to use more powerful tools for data classification, cleaning and preparing data to be used in models, which enhances a better data evaluation, thus making proper decisions. In addition, simultaneous simulations are sometimes performed, aiming to have optimization and sensitivity analysis during the history matching process. Multi-functional works are required to meet all these deficiencies. Upgrading conventional reservoir engineering approaches with CPUs, or more powerful computers are insufficient since it increases computational cost and is time-consuming. Machine learning techniques have been proposed as the best solution for strong learning capability and computational efficiency. Recently developed algorithms make it possible to handle a very large number of data with high accuracy. The most widely used machine learning approaches are: Artificial Neural Network (ANN), Support Vector Machines and Adaptive Neuro-Fuzzy Inference Systems. In this study, these approaches are introduced by providing their capability and limitations. After that, the study focuses on using machine learning techniques in unconventional reservoir engineering calculations: Reservoir characterization, PVT calculations and optimization of well completion. These processes are repeated until all the values reach to the output layer. Normally, one hidden layer is good enough for most problems and additional hidden layers usually does not improve the model performance, instead, it may create the risk for converging to a local minimum and make the model more complex. The most typical neural network is the forward feed network, often used for data classification. MLP has a learning function that minimizes a global error function, the least square method. It uses back propagation algorithm to update the weights, searching for local minima by performing a gradient descent (Figure 1). The learning rate is usually selected as less than one.

show abstract

Machine Learning for Progressive Cavity Pump Performance Analysis: A Coal Seam Gas Case Study

Cited by 2 publications

References 6 publications

The Health Index Prediction Model and Application of PCP in CBM Wells Based on Deep Learning

The Health Index Prediction Model and Application of PCP in CBM Wells Based on Deep Learning

A Thorough Review of Machine Learning Applications in Oil and Gas Industry

Contact Info

Product

Resources

About