Getting the maximum possible recovery from existing developments safely is at the heart of the Wells Reservoirs and Facilities Management (WRFM) processes of Shell Companies. WRFM in Shell seeks to keep deferments and natural production decline rates as low as possible and optimise production in line with field WRFM philosophy (minimal pressure depletion, keeping producing GOR below a threshold, keeping well head pressure and surface vessels within a defined envelope, etc). At the foundation of this efficient WRFM lies a thorough and current understanding of the entire production system, one of the ways by which this is achieved is via a system of multi-discipline (daily, quarterly, annual and ad-hoc) reviews developed within Shell and for Wells, Reservoirs and Facilities Management to enable quick understanding of deviations and quick decision making and quick corrective actions.These reviews are applied in the field under discussion. The King Field is located offshore Nigeria and has been operated for over a decade. Increasing water production with an attendant oil production decline is the most critical concern in the field. Good WRFM practices have therefore become more important than ever to safely and optimally produce hydrocarbons from the field.In one reservoir, decisions and actions following from systematic reviews resulted in the restoration of production to two producers and increased production from two other producers by artificial lift opportunity identification and execution in the wells with the following benefits:• Increase in reservoir ultimate recovery. • Optimal use of installed FPSO capacity.Similar applications of structured multi-discipline reviews to developed reservoirs have the potential to improve the recovery efficiency of these reservoirs using "common place" technologies such that a small increase in recovery factors could cumulatively result in an immense increase in production from developed fields at minimal expenditures.

Considering the imminent end of the ‘easy oil’ era, the increasing demand for energy and the global push towards the energy transition, oil and gas companies are more than ever interested in sustainable ways to develop marginal and complex hydrocarbon fields economically, through the application of technology and maximization of data analysis. In small partially appraised fields where the cost of drilling an appraisal well could derail the project economics, it becomes necessary to sweat the limited data available for reservoir modelling. The uncertainty analysis must be robust enough to ensure that the adopted field development strategy would yield a positive net present value despite the wide uncertainties associated with the field. The conventional workflow for subsurface uncertainty modelling involves defining the uncertainty ranges of static and dynamic reservoir parameters based on a single reservoir model concept. This paper focuses on a marginal field case study where the multi scenario modelling approach was adopted. This approach considered alternate reservoir geologic concepts based on different interpretations of the reservoir architecture, taking full cognizance of the available data, reservoir uncertainties and regional geology knowledge. Field Alpha is located onshore of Niger Delta in Nigeria. The geologic setting consists mainly of multi-storey, complex channel-belt systems, incising through Shoreface deposits. The reservoir of interest is an elongated structure with only two well penetrations located at the opposite distal part of the structure. The key reservoir uncertainties are reservoir structure, architecture, connectivity, and property distribution. Two possible distinct architecture were interpreted based on regional correlation and seismic. This paper focuses on how the interpretations and other information informed a robust development strategy that yielded significant (30 %) reduction in development cost and positive net present value.

Oil reservoirs developed under a natural depletion scheme usually leave behind a significant portion of the discovered oil and while in-fill drilling is a logical choice to recovering more oil from these reservoirs, there is always the challenge of locating the remaining oil and optimally placing new wells within these ‘sweet spots’. 3-Dimensional (3-D) dynamic modelling and 4-D seismic are well developed methods of establishing post-production saturation distributions and a combination of both methods further increase the confidence of the post-production saturation distributions predicted. Even then however, these methods are not infallible. Experimental design methods when integrated into dynamic modelling enable a logical and systematic interpretation/utilisation of all available data to understand the extent and impact of uncertainties, an understanding which can help make robust decisions about these re-developments. The reservoir of interest (Level 1X) is an oil rim reservoir with a 36-year production history from 6 wells. All the existing completions in the reservoir were used to develop the oil-rim. To evaluate the option of developing the gas cap of the reservoir, a technical review of the oil rim had to be carried out to assess the existing developments and evaluate the possibility of further development opportunities in the oil rim prior to the gas cap blow down. Over the area of this field, 4-D seismic was not available and while the traditional dynamic reservoir modelling method could be used to plan further development, the oil rim nature of the reservoir coupled with reservoir uncertainties (such as oil-water and gas-oil contacts) meant a second method would give further credence to any analyses of the reservoir and the proposals resulting from such analyses. This paper demonstrates how experimental design, incorporated into dynamic simulation, has been applied to the reservoir study to achieve the following: Integration of practically all data into the study (not a pre-selected value for each uncertainty) Allowing available data to guide the history match of the reservoir (History matches evolve/drop out of data) thus increasing confidence in the calibrated (low, mid and high case) models. Allowing a selection of a high confidence case re-development strategy.