scholarly journals Electric submersible pump as an effective artificial lift method to control bottom-hole pressure in a producing gas hydrate well, JOGMEC/NRCan/Aurora Mallik 2007-2008 Gas Hydrate Production Research Well Program

2012 ◽  
Author(s):  
M Rojas ◽  
C K Martin ◽  
L Hernandez-Johnson ◽  
D I Ashford ◽  
J F Wright ◽  
...  
Keyword(s):  
Gas Hydrate ◽  
Submersible Pump ◽  
Hole Pressure ◽  
Bottom Hole Pressure ◽  
Bottom Hole ◽  
Artificial Lift ◽  
Production Research ◽  
Electric Submersible Pump

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 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.

scholarly journals A Unified Formula for Determination of Wellhead Pressure and Bottom-hole Pressure

2013 ◽  
Vol 37 ◽  
pp. 3291-3298 ◽  
Author(s):  
Mingze Liu ◽  
Bing Bai ◽  
Xiaochun Li
Keyword(s):  
Wellhead Pressure ◽  
Hole Pressure ◽  
Bottom Hole Pressure ◽  
Bottom Hole
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