Estimation of Bottom Hole Pressure in Electrical Submersible Pump Wells using Machine Learning Technique

2021 ◽  
Author(s):  
Sherif Sanusi ◽  
Adenike Omisore ◽  
Eyituoyo Blankson ◽  
Chinedu Anyanwu ◽  
Obehi Eremiokhale
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Oil And Gas Industry ◽  
Supervised Machine Learning ◽  
Power Cable ◽  
Submersible Pump ◽  
Hole Pressure ◽  
Bottom Hole Pressure ◽  
Bottom Hole ◽  
Electrical Submersible Pump

Abstract With the growing importance and application of Machine Learning in various complex operations in the Oil and Gas Industry, this study focuses on the implementation of data analytics for estimating and/or validating bottom-hole pressure (BHP) of Electrical Submersible Pump (ESP) wells. Depending on the placement of the ESP in the wellbore and fluid gravity of the well fluid, there can be little or no difference between BHP and Pump intake Pressure (PIP); hence these two parameters were used interchangeably. The study focuses majorly on validating PIP when there are concerns with downhole gauge readings. It also has application in estimating PIP when the gauge readings are not available, provided the relevant ESP parameters are obtainable. ESP wells generally have gauges that operate on "Comms-on-Power" principle i.e. downhole communication is via the power cable and loss of signal occurs when there is no good electrical integrity along the electrical path of the ESP system. For proper hydrocarbon accounting and statutory requirements, it is important to have downhole pressure readings on a continuous basis, however this cannot be guaranteed throughout the life cycle of the well. Therefore, an alternative method is essential and had to be sought. In this study, the Response Surface Modelling (RSM) was first used to generate a model relating the ESP parameters acquired real-time to the PIP values. The model was fine-tuned with a Supervised Machine Learning algorithm: Artificial Neural Network (ANN). The performance of the algorithms was then validated using the R-Square and Mean Square Error values. The result proves that Machine Learning can be used to estimate PIP in a well without recourse to incurring additional cost of deploying new downhole gauges for acquisition of well and reservoir data.

scholarly journals Multiphase gas-flow model of an electrical submersible pump

2018 ◽  
Vol 73 ◽  
pp. 29
Author(s):  
Diana Marcela Martinez Ricardo ◽  
German Efrain Castañeda Jiménez ◽  
Janito Vaqueiro Ferreira ◽  
Pablo Siqueira Meirelles
Keyword(s):  
Gas Flow ◽  
Oil And Gas ◽  
Learning Algorithm ◽  
Estimation Error ◽  
Oil And Gas Industry ◽  
Support Vector ◽  
System Response ◽  
Submersible Pump ◽  
Two Phase ◽  
Electrical Submersible Pump

Various artificial lifting systems are used in the oil and gas industry. An example is the Electrical Submersible Pump (ESP). When the gas flow is high, ESPs usually fail prematurely because of a lack of information about the two-phase flow during pumping operations. Here, we develop models to estimate the gas flow in a two-phase mixture being pumped through an ESP. Using these models and experimental system response data, the pump operating point can be controlled. The models are based on nonparametric identification using a support vector machine learning algorithm. The learning machine’s hidden parameters are determined with a genetic algorithm. The results obtained with each model are validated and compared in terms of estimation error. The models are able to successfully identify the gas flow in the liquid-gas mixture transported by an ESP.

A Machine Learning Method to Infer Inter-Well Connectivity Using Bottom-Hole Pressure Data

2020 ◽  
Vol 142 (10) ◽  
Author(s):  
Wei Liu ◽  
Wei David Liu ◽  
Jianwei Gu
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Flow Direction ◽  
Time Lag ◽  
Time Lags ◽  
Pressure Data ◽  
Actual Case ◽  
Hole Pressure ◽  
Bottom Hole Pressure ◽  
Bottom Hole

Abstract In the production and development of oil fields, production wells generally produce at a constant rate since the fixed production is easier to control than the fixed pressure. Thus, it is more feasible to use bottom-hole pressure data for connectivity analysis than historical injection and production data when producers are set in fixed rates. In this work, a practical procedure is proposed to infer inter-well connectivity based on the bottom-hole pressure data of injectors and producers. The procedure first preprocesses the bottom-hole pressure based on nonlinear diffusion filters to constitute the dataset for machine learning. An artificial neural network (ANN) is then generated and trained to simulate the connection relationship between the producer and its adjacent injectors. The genetic algorithm (GA) is also introduced to avoid the tedious process of determining time lags and other hyper-parameters of ANN. In particular, the time lag is normally determined by subjective judgment, which is optimized by GA for the first time. After optimizing the parameters, the sensitivity analysis is performed on the well-trained ANN to quantify inter-well connectivity. For the evaluation and verification purposes, the proposed GA and sensitivity analysis based ANN were applied to two synthetic reservoirs and one actual case from JD oilfields, China. The results show that the calculated connectivity conforms to known geological characteristics and tracer test results. And it demonstrates that the presented approach is an effective alternative way to characterize the reservoir connectivity and determine the flow direction of injected water.

Electrical Submersible Pump Prognostics and Health Monitoring Using Machine Learning and Natural Language Processing

2021 ◽  
Author(s):  
Rajeev Ranjan Sinha ◽  
Supriya Gupta ◽  
Praprut Songchitruksa ◽  
Saniya Karnik ◽  
Amey Ambade
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Operating Conditions ◽  
Learning Approaches ◽  
Submersible Pump ◽  
Gas Industry ◽  
Electrical Submersible Pump ◽  
Low Efficiency

Abstract Electrical Submersible Pump (ESP) systems efficiently pump high volumes of production fluids from the wellbore to the surface. They are extensively used in the oil and gas industry due to their adaptability, low maintenance, safety and relatively low environmental impact. They require specific operating conditions with respect to the power, fluid level and fluid content. Oilfield operation workflows often require extensive surveillance and monitoring by subject-matter experts (SMEs). Detecting issues like formation of unwanted gas and emulsions in ESPs requires constant analysis of downhole data by SMEs. The lack of adequate and accurate monitoring of the downhole pumps can lead to low efficiency, high lifting costs, and frequent repair and replacements. There are 3 workflows described in the paper which demonstrate that the maintenance costs of the ESPs can be significantly reduced, and production optimized with the augmentation of machine learning approaches typically unused in ESP surveillance and failure analysis.

scholarly journals Predictive Modelling of Employee Turnover in Indian IT Industry Using Machine Learning Techniques

2019 ◽  
Vol 23 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Shikha N. Khera ◽  
Divya
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Predictive Modelling ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
It Industry ◽  
Knowledge Based ◽  
Employee Attrition

Information technology (IT) industry in India has been facing a systemic issue of high attrition in the past few years, resulting in monetary and knowledge-based loses to the companies. The aim of this research is to develop a model to predict employee attrition and provide the organizations opportunities to address any issue and improve retention. Predictive model was developed based on supervised machine learning algorithm, support vector machine (SVM). Archival employee data (consisting of 22 input features) were collected from Human Resource databases of three IT companies in India, including their employment status (response variable) at the time of collection. Accuracy results from the confusion matrix for the SVM model showed that the model has an accuracy of 85 per cent. Also, results show that the model performs better in predicting who will leave the firm as compared to predicting who will not leave the company.

scholarly journals Production behavior evaluation on multilayer commingled stress-sensitive carbonate gas reservoir

2020 ◽  
pp. 014459872096415
Author(s):  
Jianlin Guo ◽  
Fankun Meng ◽  
Ailin Jia ◽  
Shuo Dong ◽  
Haijun Yan ◽  
...  
Keyword(s):  
Early Stage ◽  
Sedimentary Environment ◽  
Production Performance ◽  
Total Production ◽  
Inversion Algorithm ◽  
Gas Reservoir ◽  
Hole Pressure ◽  
Bottom Hole Pressure ◽  
Bottom Hole ◽  
Production Behavior

Influenced by the complex sedimentary environment, a well always penetrates multiple layers with different properties, which leads to the difficulty of analyzing the production behavior for each layer. Therefore, in this paper, a semi-analytical model to evaluate the production performance of each layer in a stress-sensitive multilayer carbonated gas reservoir is proposed. The flow of fluids in layers composed of matrix, fractures, and vugs can be described by triple-porosity/single permeability model, and the other layers could be characterized by single porosity media. The stress-sensitive exponents for different layers are determined by laboratory experiments and curve fitting, which are considered in pseudo-pressure and pseudo-time factor. Laplace transformation, Duhamel convolution, Stehfest inversion algorithm are used to solve the proposed model. Through the comparison with the classical solution, and the matching with real bottom-hole pressure data, the accuracy of the presented model is verified. A synthetic case which has two layers, where the first one is tight and the second one is full of fractures and vugs, is utilized to study the effects of stress-sensitive exponents, skin factors, formation radius and permeability for these two layers on production performance. The results demonstrate that the initial well production is mainly derived from high permeable layer, which causes that with the rise of formation permeability and radius, and the decrease of stress-sensitive exponents and skin factors, in the early stage, the bottom-hole pressure and the second layer production rate will increase. While the first layer contributes a lot to the total production in the later period, the well bottom-hole pressure is more influenced by the variation of formation and well condition parameters at the later stage. Compared with the second layer, the scales of formation permeability and skin factor for first layer have significant impacts on production behaviors.

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