Improve Completion Decisions with Enhanced Dynamic Reservoir Modelling and Effective Reservoir Management While Drilling: Integrating Downhole Petrophysics with Advanced Surface Mud Gas Logging (AMGL) Techniques

Abstract

Abstract In a brownfield, it is common to observe the movement of fluid contacts and alterations in petrophysical behaviors resulting from production and injection activities. The acquisition of field surveillance information through well intervention in a deep-water field with subsea wells is limited as it comes with a significant cost and poses a substantial risk to the asset. Furthermore, burdened by limitations in gas handling facilities and challenges associated with water emulsions, the primary objective for new infill wells is to achieve the minimum feasible gas-oil ratio (GOR) and water cut. Thus, innovative data acquisition while drilling infill wells, such as strategically penetrating shallower producing reservoirs close to the anticipated fluid contacts without risking the primary well objective, is required to de-risk uncertainties. Advanced mud gas logging (AMGL) has been and remains to be a reliable surface measurement to log and determine downhole fluid composition. New workflows using AMGL, combining extraction efficiency calibrations before drilling with mud gas isotope logging (MGIL), enable an improved, consolidated application for real-time insight to the fluid properties. This paper presents the use of reliable geochemical signatures together with downhole petrophysical interpretation while drilling in a mature oilfield, leading to more effective reservoir management. Continuous fluid properties from AMGL coupled with MGIL and integration with logging while drilling (LWD) measurements allowed to timely assure the existing dynamic reservoir model and subsequently optimize well completion, steering production towards zones with low GOR and low water cut. Specific mud gas ratios utilized to qualify water intervals and identify fluid type aided landing decisions also when LWD information was not available. In addition, AMGL was able to identify the fluid composition, with high certainty, from tight rock intervals. The gas cap formed due to gas expansion and gas injection was also identified by combination of AMGL and MGIL measurements result. Establishing a numerical model from laboratory, derived from pressure-volume-temperature (PVT) analysis of a downhole fluid, then implementation of these cut offs on AMGL data while drilling, highlighted a potential for being implemented as an industry standard for effective reservoir management. Real-time AMGL and petrophysical information were combined to enable an integrated and holistic view of the reservoir and fluid fills.