Optimising the late-life development of heavy oil reservoirs due to biodegradation in a fresh water aquifer remains a challenge in the industry. When the variation of viscosity with depth is coupled with a significant degree of compartmentalisation due to structural complexities, the identification of a technically and economically viable development requires an integrated approach in field development studies. This paper presents a case study for such a complex field, a Gharif reservoir situated in the Eastern Flank of the South Oman Salt Basin. The integration between various data sets from across disciplines of varying fidelity and by adopting a decision-based planning approach has achieved two outcomes. Firstly, the highest field production since coming on stream; and secondly, the delivery of an updated Field Development Plan (FDP) that unlocks remaining hydrocarbon potential in a phased approach to mitigate key risks. On stream since 1981, the heavily compartmentalized Marmul Gharif South Rim Field has evolved from a primary depletion to a mature waterflood by flank injection. The sands, distributed in a rim setting with a steep dip tend to be vertically discontinuous in the wells, so that direct observation of fluid contacts is very rare and most wells yield only a Water Up To (WUT) or Oil Down To (ODT). In addition, the poor contrast of heavy oil density against fresh formation water makes it difficult to obtain accurate pressure gradients. The field can be subdivided into a number of compartments with varying degrees of communication from complete hydraulic independence to weak/moderate pressure communication. Over the course of 2016-17, a study was carried out by a multi-disciplinary team to deliver a FDP. By integrating existing data, the team created a new structural framework. This involved integrating faults based on Bore Hole Images (BHI) together with seismic re-interpretation; analyzing production and pressure data for connectivity mapping; updating the OWC assessment which considered oil biodegradation as a function of height above free water level. This was followed by combining the new insights into a fit-for-purpose dynamic modelling approach which led to the identification of new infill/appraisal targets and formed the basis of the redevelopment plan. The increased understanding of the field has allowed early WRFM activities which contributed to increase production by the order of 20%. The effort has materialized into an improved field understanding and delivered a rejuvenation plan with an immediate impact of unlocking reserves with the drilling of 5 drilling & appraisal targets in 2017. This is followed by a phased development with 30 development and 3 appraisal wells in Phase 1; and additional 65 development and 1 appraisal well in Phase 2, to increase the field recovery factor by 5%.

Unlocking the potential of existing assets and efficient production optimisation can be a challenging task from resource and technical execution point of view when using traditional static and dynamic modelling workflows making decision-making process inefficient and less robust. A set of modern techniques in data processing and artificial intelligence could change the pattern of decision-making process for oil and gas fields within next few years. This paper presents an innovative workflow based on predictive analytics methods and machine learning to establish a new approach for assets management and fields’ optimisation. Based on the merge between classical reservoir engineering and Locate-the-Remaining-Oil (LTRO) techniques combined with smart data science and innovative deep learning algorithms this workflow proves that turnaround time for subsurface assets evaluation and optimisation could shrink from many months into a few weeks. In this paper we present the results of the study, conducted on the Z field located in the South of Oman, using an efficient ROCM (Remaining Oil Compliant Mapping) workflow within an advanced LTRO software package. The goal of the study was to perform an evaluation of quantified and risked remaining oil for infill drilling and establish a field redevelopment strategy. The resource in place assessment is complemented with production forecast. A neural network engine coupled with ROCM allowed to test various infill scenarios using predictive analytics. Results of the study have been validated against 3D reservoir simulation, whereby a dynamic sector model was created and history matched. Z asset has a number of challenges starting from the fact that for the last 25 years the field has been developed by horizontal producers. The geological challenges are related to the high degree of reservoir heterogeneity which, combined with high oil viscosity, leads to water fingering effects. These aspects are making dynamic modelling challenging and time consuming. In this paper, we describe in details the workflow elements to determine risked remaining oil saturation distribution, along with the results of ROCM and a full-field forecast for infill development scenarios by using neural network predictive analytics validated against drilled infills performance.