Scale Treatment in a Mature Field: A Case Study on Collaborative Candidate Selection

Abstract Field A, with a production history spanning over two decades, stands as a mature asset in the Malay Basin. The operator has focused on enhancing well productivity throughout this period. Among the various methodologies employed for production optimization, scale treatment plays a significant role. In this study, a comprehensive methodology and workflow were developed for scale treatment, encompassing candidate selection, well diagnosis, execution, and post-job evaluation. The candidate screening exercise employs a rapid and integrated screening process using production software that covers field-level to well-level assessment. This workflow uses an automated filtering process to gain an improved field-level understanding of each reservoir by using available petrophysical, reservoir, and production information. Once the underperforming candidates are screened, a detailed well evaluation is performed, starting from historical well performance modeling using a multiphase steady-state simulator, damage characterization, water analysis, and type of scale simulation. This analysis enables us to determine the type and the most dominant scale present across the perforations to the surface choke. The evaluation and diagnostic processes have provided invaluable insights to the intervention team. These insights have guided decisions regarding fluid selection, the design of pumping schedules, and the sequencing of operations to optimize acidizing treatment fluid contact with each zone. Zonal treatments were administered using the bullheading and coiled tubing wash technique. Following the treatment, the wells were brought back online, and zonal well testing was conducted to assess pretreatment and posttreatment performance. Remarkably, postexecution well tests for two wells demonstrated a significant increase in oil rates, underscoring the efficacy of the treatment. A thorough postjob evaluation was carried out, incorporating updates to the well model to analyze skin reduction and productivity index increments, thereby closing the study loop. The solution implemented in this case study has showcased a robust, sustainable, and replicable methodology. It unequivocally illustrates that thorough candidate evaluation mitigates uncertainties, streamlines well problem identification, and ultimately facilitates the selection of the most suitable and cost-effective remedial treatment approach. Field A is an aging brownfield where production rates have been steadily declining. This decline is primarily attributed to various production challenges, with the most significant issues stemming from formation damage and wellbore restrictions. A comprehensive analysis was conducted to thoroughly understand the nature and extent of the damage, enabling the development of tailored treatment recommendations aimed at enhancing well productivity. To maintain data confidentiality, the authors have chosen to omit all specific information and datasets related to this project. Instead, the methodology is presented using a generic example, illustrating the workflow while preserving the integrity of the confidential data.