In ACS omega
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 not 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.
Alharbi Omar Q, Alarifi Sulaiman A
2023-Feb-21
