In today's fast paced and challenging oil industry, the need of faster evaluation studies for quick generation of field development plan (FDP) is becoming more crucial to remain competitive. Field's geological and structural complexity, uncertainty of production data adds to the challenges. Traditional approach of building dynamic mesh models carrying out numerical simulation to history match, then predict has always remained time consuming in large mature fields. The ‘B’ field in Peninsular Malaysia is a mature clastic with stacked reservoirs having a huge gas cap with moderate aquifer. Significant production over last 30+ years led to uneven movement of the gas cap and also of the edge aquifer leading to possibility of bypassed oil. The updated dynamic model could not match the preferential gas cap movement, thus failed to match the high GOR of downdip wells and also unable to match high watercut of certain updip wells. To identify the areas of bypassed oil thus is a significant challenge with the current dynamic model. New engineering tools of polygon balancing, material balance, normalized EUR bubbles were used with the 3D static model volume and the facies understanding. The uncertainties and risks were also identified and clear measurable methods were proposed to address the uncertainties and reduce the risks. Very detailed decision tree with clear data gathering plan to drill successive optimum wells have been planned during the campaign. This paper details the new engineering tools used to delineate and quantify the bypassed oil in these huge clastic reservoir with preferential gas and water movement, unable to be history matched by the dynamic model. It explains the engineering methods applied to identify and quantify the 10 infill wells proposed for the development campaign. To reduce risks, this paper would also explain the blind testing that was carried out on for this new reservoir engineering analysis tool by deriving the infill potentials of the previous campaign (4 years back) by the same method. The paper details how robust technical development plans were generated having infill well locations and reserve determination. This paper will also demonstrate the classic "Do-Learn-Adapt" strategy through its infill wells prioritization & ranking, subsurface de-risking analysis, data acquisition and mitigations plans.

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Field A consists of multi stacked reservoirs in high geological complexity and heterogeneity setting, with waterflooding has been the secondary drive mechanism for the past two decades. However, in recent years, the field experiencing significant production decline that warrant immediate mitigation plan and action. Therefore, this paper highlights challenges and best practices in rejuvenating water injected reservoir to improve field production by integrating geological re-interpretation, data acquisition and analytical evaluation. The reservoir is defined in deltaic environment with complex fluvial reservoir architecture. Despite no indication of structural trap or compartmentalization, there is significant variation in reservoir performance across the field indicates lateral heterogeneity that is affecting the areal sweep efficiency. Poor production-injection allocation data due to commingled production, aggravated by tubing leaks have hindered for an optimum formulation of waterflood strategy in the past. As part of the mitigation plan, depo-facies definition and stratigraphy boundaries were further refined, guided by well and reservoir pressure performance. Besides, inter-well tracer injection implementation proved to be the game changer - unfolded hydrodynamic connectivity and flow path of injected water understanding, established actual producer and injector pairing, and identified poor or unswept areas. It was supported by comprehensive analytical water injection performance analysis including Hall's Plot, Chan's Plot, Jordan's Plot as part of the routine surveillance activities to trigger any non-conformance. More aggressive well intervention also helped to identify and rectify well issues. As the outcomes, there is opportunity to increase water injection rate by 30% field wide by reactivating idle wells, converting producers to injector, and maximizing the existing injection within the safe fracture limit. The subsurface risks on fracture gradient uncertainty and sweep inefficiency due to water cycling to be mitigated via injectivity test with gradual injection, close monitoring of liquid rate handling at surface, and pattern balancing between injectors and producers. The liquid rate is expected to be restored and sustained nearing the historical peak, hence improve field production and temper the decline. This paper presents the best practices to address the challenges in a matured waterflood reservoirs, considering the complex geology setting. Understanding of the flood pattern from tracer analysis, supplemented by producer-injection performance assessment and well integrity status validation enabled water injection to be ramped up at the right area in strategically and safely manner.

History Matching (HM) is one of the critical steps for dynamic reservoir modelling to establish a reliable predictive model. Numerous approaches have emerged over the decades to accomplish a robust history matched reservoir model ranging from the classical reservoir engineering approach to the widely accepted 3D numerical simulation approach and its variations. As geological complexity of the oil and gas field increases (multilayered reservoirs, heavily faulted) compounded with completion complexity (dual strings, commingle production), building a fully representative predictive reservoir model can be arduous to almost impossible task. Artificial Intelligence (AI) and machine learning has advanced almost all major industries, including the petroleum industry in general and reservoir engineering. The objective of this paper is to outline a novel approach in history matching using a data-driven approach through Artificial Intelligence via Artificial Neural Network (ANN) and Data-Driven Analytics. In this paper, a step by step methodology in building a reservoir model and history matching process using ANN will be described which includes data preparation and data QA/QC, spatiotemporal database formulation, reservoir model design, ANN architecture design, model training and history matching strategy. A case study of the implementation to Field "A" in Malaysian waters is presented where good to fair history matching quality was obtained for all production parameters. Field "A" is a 25kmx75km oil sandstone reservoirs of a highly geologically complex field (more than 200 major and minor faults, more than 30 reservoir layers) of more than 25 years of production. The challenges of history matching of this field does not only lie on its geologically complex structure and its corresponding subsurface uncertainties, but also on the production strategy of the wells that involved commingled dual strings production with several integrity issues that adds additional dimensions to the field's complexities. To date, Field "A" has no field wide history matched reservoir model using conventional numerical simulation method available due to the complexity of history matching. This long history matching woe is mitigated via the implementation of AI based reservoir model and Data Analytics. This novel approach is estimated to be more time and cost-efficient compared to the conventional method. The comparison of this AI based reservoir model and history matching methodology with the conventional numerical reservoir model approach will be discussed. Furthermore, the advantages, limitations and areas of improvements of this AI based history matching methodology will also be highlighted. The target audience of this paper would be to reservoir engineering practitioners and dynamic model simulators who is interested to learn the complementary or alternative approach in reservoir modelling apart from conventional numerical modelling in order to create time-efficient reservoir model and reducing the risks in their field development plans.

History matching is one of the paramount steps in reservoir model validation to describe, analyze and mimic the overall behavior of reservoir performance. Performing history matching on highly faulted and multi layered reservoirs is always challenging, especially when the wells are completed with multiple zones either with single selective or dual strings. The history matching complexity is also compounded with uncertainties in production allocation, well history and downhole equipment integrity overtime. It is a common practice for deterministic history matching in reservoir numerical simulation to modify the both static and dynamic model parameters within the subsurface uncertainty window. However, for multi layered reservoirs completed with dual strings, another parameter that is most often get overlooked is the completion string’s leaking phenomenon that tremendously impacting the history matching. The objective of this paper is to introduce dual strings leaking diagnostics methodology from various disciplines’ angles. We demonstrate these dual strings leaking phenomenon impact on history matching. This paper covers dual strings leak diagnostic methodology which includes production logging tool evaluation, well’s production performance and recovery factor analysis. Possible factors that gives rise to the string’s leaks including material corrosion from high CO2 and sand production will also be discussed. We will demonstrate on how the leak phenomenon could be mimicked in the reservoir numerical model. Possible risks on future infill well identification if the leaks phenomenon is not incorporated will be also discussed. The dual strings leaks diagnosis and application in numerical simulation is illustrated on a case study of Field "D", a multilayered sandstone reservoir in Malaysia of almost 3 decades of production. This proven leak identification and reservoir model history matching methodology has been replicated for all the fault blocks across the field. It potentially unlocks more than 100 MMSTB of additional oil recovery by drilling more oil producers and water injectors in future drilling campaigns.