The Use Of Pressure Data From Permanently Installed Bottom Hole Pressure Gauges

1963 ◽  
Author(s):  
W.A. Nestlerode
Keyword(s):  
Pressure Data ◽  
Hole Pressure ◽  
Bottom Hole Pressure ◽  
Bottom Hole

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.

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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