Neural Networks in BHCP Prediction Performed Much Better Than Mechanistic Models

Ashena, Rahman; Moghadasi, Jamshid; Ghalambor, Ali; Bataee, Mahmood; Ashena, Rahim; Feghhi, Amir

doi:10.2523/130095-ms

Cited by 5 publications

(1 citation statement)

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“…CI can address such complications with relative ease . Some of the domains of the petroleum engineering in which CI techniques brought new innovations include porosity-permeability predictions (Abdulraheem et al 2007;El-Sebakhy et al 2012;Nooruddin et al 2013;Helmy et al 2013;Anifowose et al 2013Anifowose et al , 2014Anifowose et al , 2017, hydraulic flow unit identification (Shujath Ali et al 2013), rock mechanical parameters estimation (Yang and Rosenbaum 2002;Sonmez et al 2004;Abdulraheem et al 2009;Cevik et al 2011;Tariq et al 2018b), missing petrophysical well logs estimation (Tariq et al 2019), welltesting parameters estimation (Artun 2017;Bazargan and Adibifard 2017), asphaltene and wax precipitation predictions (Rezaian et al 2010;Adeyemi and Sulaimon 2012;Fattahi et al 2015;Alimohammadi et al 2017), water saturation prediction (Adebayo et al 2015;Bageri et al 2015;Baziar et al 2016Baziar et al , 2018Khan et al 2018), gas compressibility factor (Mohagheghian et al 2015;Tariq and Mahmoud 2019), oil well drilling rate of penetration optimization (Gidh et al 2012), and many other oil and gas applications (Ashena et al 2010;Jahanandish et al 2011;Asoodeh 2013;Rammay and Abdulraheem 2016).…”

Section: Introductionmentioning

confidence: 99%

Real-time prognosis of flowing bottom-hole pressure in a vertical well for a multiphase flow using computational intelligence techniques

Tariq

Mahmoud

Abdulraheem

2019

J Petrol Explor Prod Technol

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An accurate prediction of well flowing bottom-hole pressure (FBHP) is highly needed in petroleum engineering applications such as for the field production optimization, cost per barrel of oil reduction, and quantification of workover remedial operations. A good number of empirical correlations and mechanistic models exist in the literature and are frequently used in oil industry to estimate FBHP. But majority of the empirical models were developed under a laboratory scale and are therefore inaccurate when scaled up for the field applications. The objective of this study is to present a new computational intelligence-based model to predict FBHP for a naturally flowing vertical well with multiphase flow. The present study shows that the accuracy of FBHP estimation using PSO-ANN is better than the conventional ANN model. A small average absolute percentage error of less than 2.1% is observed with the proposed model, while comparing the previous empirical correlations and mechanistic models on the same data gives more than 15% error. The new model is trained on a surface production data, which makes the prediction of FBHP in a real time. A group trend analysis tests were also carried out to assure that the proposed model is accurately capturing the underline physics behind the problem.

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