Jun Risang Endo

ANALISIS INJEKSI AIR LAPANGAN JE BLOK T LAPISAN S MENGGUNAKAN SIMULASI RESERVOIR

Introduction

Analisis injeksi air lapangan je blok t lapisan s menggunakan simulasi reservoir. Optimalkan produksi minyak Lapangan JE Blok T dengan analisis injeksi air dan simulasi reservoir. Temukan skenario waterflooding terbaik untuk meningkatkan recovery factor.

Abstract

AbstrakLapisan S yang terletak pada Lapangan JE blok T merupakan suatu reservoir minyak produktif mulai dari tahun 1971 hingga 2016. Tetapi, penurunan produksi minyak terjadi pada tahun 2016 sehingga upaya waterflooding perlu dilakukan. Penelitian ini dimaksudkan untuk mendesain pola waterflood menggunakan model dinamis lapangan dengan melakukan sensitivitas laju injeksi air dan pola injeksi agar menghasilkan produksi minyak yang optimal. Penelitian dilakukan dengan metode simulasi reservoir menggunakan CMG 2015 black oil simulator. Skenario yang dibuat sebanyak dua skenario yang dijalankan hingga tahun 2035. Skenario pertama yaitu menambahkan 12 sumur produksi dan 4 sumur injeksi. Skenario kedua yaitu menambahkan 13 sumur produksi dan 8 sumur injeksi. Hasil dari penelitian menunjukkan bahwa skenario pertama dengan laju alir injeksi air 50 bwpd mendapatkan hasil incremental recovery factor sebesar 12,46% atau 2,23 MMstb; skenario kedua dengan laju injeksi alir air 50 bwpd sebesar 12,64% atau 2,27 MMstb. Sehingga, skenario terbaik yang didapatkan adalah skenario kedua.Kata kunci: waterflooding, incremental recovery factor, simulasi reservoir, laju alir injeksi air, pola injeksi AbstractLayer S in Field JE block T is a productive oil reservoir since 1971. However, oil production decreased in 2017. Therefore, waterflood is needed. The purpose of research is to design waterflood pattern and injection rate by sensitivity to optimize oil production. Research conducted by reservoir simulation. Two injection scenario is made to 2035. First scenario conducted by adding 12 production wells and 4 injection wells; second scenario is conducted by adding 13 production wells and 8 injection wells. The best scenario is second scenario which get 12,64% of incremental recovery factor or 2,27 MMstb and injection rate 50 bwpd. Kata kunci: waterflooding, incremental recovery factor, reservoir simulation, water injection rate, injection pattern

Review

This paper presents a timely and relevant study on optimizing oil production from the Layer S reservoir in Field JE, Block T, which has experienced a significant decline since 2016/2017. The core objective of designing an effective waterflood strategy through reservoir simulation to maximize oil recovery is well-defined and addresses a critical challenge in mature oil fields. The use of a dynamic field model and sensitivity analysis on key parameters like injection rates and well patterns indicates a robust approach to identifying optimal development plans. The research clearly articulates its aim to provide practical recommendations for field development, making a valuable contribution to reservoir management practices for similar assets. The methodology employed, utilizing the CMG 2015 black oil simulator for reservoir simulation, is appropriate for this type of study. The authors have investigated two distinct scenarios over a projection period extending to 2035, demonstrating a long-term perspective on the proposed solutions. Specifically, the comparison between Scenario 1 (12 production wells, 4 injection wells) and Scenario 2 (13 production wells, 8 injection wells) at a fixed injection rate of 50 bwpd provides a clear basis for evaluating different waterflood configurations. The results, showing incremental recovery factors of 12.46% (2.23 MMstb) for Scenario 1 and 12.64% (2.27 MMstb) for Scenario 2, effectively highlight the superior performance of the latter, providing a definitive recommendation. While the abstract provides a clear overview of the study's scope and findings, several areas could be expanded upon for a more comprehensive journal submission. A detailed discussion of the reservoir characteristics, including heterogeneity, fluid properties, and initial conditions, would strengthen the basis of the simulation model. Furthermore, while injection rate sensitivity is mentioned, the abstract only presents results for a single rate (50 bwpd); exploring a wider range of rates and their impact on recovery and injectivity would provide deeper insights. Incorporating an economic analysis alongside the technical results, considering factors like Net Present Value (NPV) or project economics, would significantly enhance the practical utility of the "best scenario" determination. Finally, a brief discussion on the limitations of the black oil model or potential for uncertainty analysis (e.g., in geological parameters) would add academic rigor.

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