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A new flowing bottom hole pressure prediction model using M5 prime decision tree approach

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Abstract

Flowing bottom hole pressure (FBHP) is an indication of the fluid pressure in a porous reservoir under production. FBHP is obtained using a permanent gauge, well-testing analysis, or developing a mechanistic/correlation model. These conventional methods are costly and can be unreliable in the case of mechanistic/correlation approach. In line with this, the present study proposes the application of M5 prime for evaluating the FBHP of a well having a multiphase flow system. The proposed M5 prime FBHP model was further compared with backpropagation neural network (BPNN), generalised regression neural network (GRNN), radial basis function neural network (RBFNN), least-squares support vector machine (LSSVM), and group method of data handling (GMDH) to ascertain its performance. The M5 prime was identified as the superior technique as it attained the highest coefficient of determination (R2), correlation coefficient (R), the variance accounted for (VAF), and performance index (PI) values of 0.985, 0.99, 98.5, and 1.9, respectively, during testing. From the analysis of the contribution of each input variable, it was discovered that the tubing head pressure (THP) was the most dominant variable followed by the oil production flow rate (Qo), with the gas rate (Qg) having the least influence on the M5 prime outcome. Therefore, conclusion can be drawn that the proposed M5 prime model represents the best performing method as it can generate a more robust FBHP estimation while accounting for a superior variance of the measured data.

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Authors and Affiliations

  1. Department of Chemical and Petrochemical Engineering, School of Petroleum Studies, University of Mines and Technology, Tarkwa, Ghana

    Solomon Adjei Marfo

  2. Department of Petroleum and Natural Gas Engineering, School of Petroleum Studies, University of Mines and Technology, Tarkwa, Ghana

    Solomon Asante-Okyere

  3. Department of Geomatic Engineering, Faculty of Mineral Resources Technology, University of Mines and Technology, Tarkwa, Ghana

    Yao Yevenyo Ziggah

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  1. Solomon Adjei Marfo
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Marfo, S.A., Asante-Okyere, S. & Ziggah, Y.Y. A new flowing bottom hole pressure prediction model using M5 prime decision tree approach. Model. Earth Syst. Environ. 8, 2065–2073 (2022). https://doi.org/10.1007/s40808-021-01211-7

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