Constructing IPR for a Well Using Python

EasyChair Preprint 15675

Abstract

Using Python, a significant and reliable approach for constructing the Inflow Performance Relationship (IPR) is presented in this paper. Predicting production rate of a well as a function of its flowing bottom-hole pressure by using a fundamental tool in petroleum engineering known as IPR. Python is used in this paper to automate the process of building IPR curves through fitting mathematical models to well data. By applying our approach to well data and comparing the resulting IPR curves using Vogel’s equation with those obtained using traditional methods, will show its effectiveness. Our Python-based approach is accurate, efficient, and provides a powerful tool for IPR analysis, as shown by our results. By providing a tool that can enhance the accuracy and speed of IPR estimation, the study will contribute to the field of petroleum engineering and enable more informed decision-making in oil and gas production operations.

Keyphrases: Inflow Performance Relationship, Python programming, mathematical models

@booklet{EasyChair:15675,
  author    = {Chaitanya Deshmukh and Shailendra Naik},
  title     = {Constructing IPR for a Well Using Python},
  howpublished = {EasyChair Preprint 15675},
  year      = {EasyChair, 2025}}