An Improved Correlation to Estimate Productivity Index in Horizontal Wells

Escobar, Freddy Humberto; Saavedra, Néstor; Aranda, Robin; Herrera, John J.

doi:10.2523/88540-ms

Cited by 1 publication

(2 citation statements)

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“…anisotropy radial flow near the wellbore and linear flow far away from the wellbore damage near wellbore Escobar et al 15 i k j j j j j j j j Ä i k j j j j j j j j j j j j i k j j j j j j j j j j j Ä Alternative approaches to conventional methods were covered in the literature, including utilizing machine learning (ML) techniques. Alarifi et al introduced several models that employ machine learning methods, e.g., artificial neural network (ANN), fuzzy logic (FL), and functional networks (FN), to model the productivity index of single-lateral horizontal wells.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, horizontal wells can be very effective in scenarios where vertical wells would be considered unfavorable, e.g., (14) splitting up the drainage area into three parts. One semicircle part and two rectangle parts with different lengths Hongen 12 (15) steady state treated the horizontal well as a vertical well Lu 13 (16) anisotropy elliptical shape Furui et al 14 (17) anisotropy radial flow near the wellbore and linear flow far away from the wellbore damage near wellbore Escobar et al 15 (18) steady state based on Joshi's and Furui's models Farman and Abdulamir 16 (19) Alternative approaches to conventional methods were covered in the literature, including utilizing machine learning (ML) techniques. Alarifi et al introduced several models that employ machine learning methods, e.g., artificial neural network (ANN), fuzzy logic (FL), and functional networks (FN), to model the productivity index of single-lateral horizontal wells.…”

Section: Introductionmentioning

confidence: 99%

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Productivity Index Prediction for Single-Lateral and Multilateral Oil Horizontal Wells Using Machine Learning Techniques

Alharbi

Alarifi

2023

ACS Omega

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Horizontal wells are geometrically shaped differently and projected to different flow regimes than vertical wells. Therefore, the existing laws that govern flow and productivity in vertical wells are applicable to horizontal wells directly. The objective of this paper is to develop machine learning models that predict well productivity index using several reservoir and well inputs. Six models were developed using the actual well rate data from several wells divided into single-lateral wells, multilateral wells, and a combination of single-lateral and multilateral wells. The models are generated using artificial neural networks and fuzzy logic. The inputs used to create the models are the typical inputs used in the correlations and are well-known for any well under production. The results of the established ML models were excellent as suggested by an error analysis performed, reflecting the models to be robust. The error analysis showed high correlation coefficient values (between 0.94 and 0.95) supported by a low estimation error for four models out of six. The added value of this study is the developed general and accurate PI estimation model that overcomes many limitations of several widely used correlations in the industry and can be utilized for single-lateral or multilateral wells.

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Section: Introductionmentioning

confidence: 99%