Post-Acidizing Productivity Index Evaluation Using an Integrated Analytical and Multi-Software Nodal Approach: A Case Study of Well O-08, El-Sharara Oil Field, Libya - Nisreen Alkhoja,; Darin Touba,; Mohammed Alshaebi, (AlQalam Journal of Medical and Applied Sciences, 2026)
This study displays an accurate evaluation of the productivity index (PI) of the well O-08 located in the NC115 El-Sharara O-field, Libya, following matrix acidizing treatment. The well was shut in during 2014 due to the country’s security situation, resulting in severe formation damage around the wellbore and a zero value of the productivity index. A matrix acidizing operation for sandstone formations was conducted, and it successfully regained production when the well was reactivated. However, the absence of reliable data before treatment has been a major obstacle to obtaining accurate estimates for its effect on well productivity. The main objective of this study is to accurately determine the post-acidizing PI in order to evaluate the consistency, predictive capability, and reliability of integrated analytical and multi-software modeling techniques. An integrated approach was employed, which consisted of utilizing both the equations' analytical calculations (conducted in Microsoft Excel) and nodal analysis techniques (implemented in the PROSPER and PIPESIM software packages). The results revealed remarkable consistency across all⁠ evaluation methods: analytical calculation yielded a PI of 8.90 STB/D/psi, while PROSPER and PIPESIM produced PIs of 8.83 and 8.81 STB/D/psi, respectively, with an average of 8.82 STB/D/⁠psi, with less than 0.80% deviation⁠ from the analytical result. This high consistency confirms the validity of integrated modeling, which effectively mitigates uncertainties in data-limited conditions. The restored PI of 8.90 S⁠TB/D/psi confirms the success of the sandstone acidizing design. It is recommended that future well interventions in similar settings adopt this integrated modeling framework to enhance decision-making, improve performance forecasting, and support sustainable⁠ reservoir management under data limitations.