Integrated Surveillance and Reservoir Simulation Safeguard Value of Thin-Oil-Rim Brownfield

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 36701, “Safeguarding Value of Thin-Oil-Rim Brownfield Through Integrated Time-Lapse Surveillance and Reservoir Simulation Updates,” by Joely Abdul Ghafar, SPE, Ahmad Z. Ishak, and D.M. Yap, Petronas, et al. The paper has not been peer-reviewed. Copyright 2026 Offshore Technology Conference. _ This paper presents a case study from a mature field-redevelopment project where pulsed neutron logging (PNL), also known as contact logging, was integrated with advanced reservoir modeling to improve the understanding of fluid-contact dynamics and optimize new horizontal-well placement. PNL provided high-resolution saturation data in cased-hole environments, enabling the detection of nonuniform and tilted fluid contacts that were not captured using conventional methods. The integration of PNL data significantly improved contact matching and predictive capability, enhancing the economic viability of the redevelopment plan by reducing drilling-target risks and maximizing hydrocarbon recovery. Reservoir Redevelopment Plan The redevelopment of the mature brownfield began with the construction of a robust static reservoir model (Fig. 1). The static reservoir model served as the foundation for understanding the reservoir’s complexities and heterogeneities. By integrating the latest seismic interpretations, the model captured the subsurface structural framework with high precision. This included identifying faults, fractures, and other geological features that could influence fluid flow and reservoir performance. Core analysis provided detailed insights into reservoir rock properties such as porosity, permeability, and mineral composition. These parameters were crucial for characterizing storage capacity and fluid-flow characteristics. The updated petrophysical evaluations further refined the understanding of the reservoir by incorporating data from well logs. Data from recent infill-drilling campaigns also were integrated into the model. By incorporating this data, the model was able to reflect the dynamic changes that had occurred in the reservoir over its production history. The objective of constructing this comprehensive static reservoir model was to establish a reliable representation of the reservoir’s initial conditions. This included defining the structural framework, which delineated the geometry and boundaries of the reservoir. The facies distribution, which described the spatial variation of rock types and depositional environments, also was mapped out. Additionally, petrophysical properties were characterized to understand the reservoir’s ability to store and transmit fluids. Following the development of the static reservoir model, the next step was to initiate dynamic simulation. This phase involved the creation of a dynamic model that could simulate the behavior of fluids within the reservoir over time. The dynamic model was calibrated through a comprehensive history-matching process, which involved the integration of various types of historical data, including production rates, reservoir-pressure trends, water-cut evolution, and gas-production rates. This iterative process continued until a satisfactory match between the model predictions and the historical data was achieved.