Hooman Karkooti

How to Get the Most Out of Your Oil Rim Reservoirs

Oil production from oil rim reservoirs has always been a challenge due to their thinly spread oil resources and complicated production mechanisms. Movement of oil/water and gas/oil contacts could be very sensitive to conventional production operation and cause detrimental early water/gas breakthrough. The low oil production volume and hence low recovery (typically less than 18%) make the oil rim field development economically less attractive. However, integration of state-of-the-art engineering approaches, innovative technical initiatives and new technologies can make a significant change in the oil rim reservoir development. This paper, which is a brief of the author’s recent SPE distinguished lecture on the same topic, disseminates the applied fundamentals, critical elements and proven practices to maximize the hydrocarbon recovery in successful and integrated oil rim developments. The paper covers the reliable volumetric assessment and development concept (i.e., sequential, concurrent, etc.), robust and proactive reservoir management/monitoring policy to advice on depletion strategies and production to control the conning and cusping of water and gas. In addition, utilizing new technologies, appropriate production technology advice to assist timely development decision making, best simulation and modeling approach for the applied technologies (e.g., Horizontal/Multilateral wells, smart wells/completion, ICD/ICV, tracer, etc.) and complicated mechanism and dynamics involved in oil rim development are explained and discussed. The recommended workflow, guideline and technical initiatives will be elaborated throughout the paper and the success and value creation of the recommended methodology will be demonstrated in various real case studies. The paper demonstrates progressive and step-by-step recovery factor improvement up to additional 20% in the studied real cases.

Production Integrated Smart Completion Benchmark for Field Re-Development

For marginal field development and mature field re-development, the main art of maximizing reservoir contact is to design wells that could enable commingle production simultaneously depleting not only the major but also the selected minor sands in the field. Field implementation cases in Malaysia have been shown that this could significantly minimize the well count, increase the well productivity, and improve the ultimate recovery per well particularly in the multiple-stacked and compartmentalized reservoirs. Commingle production from several sands may have the risks and the uncertainties, among others, of layer cross-flow, excessive GOR production and early water breakthrough at certain sand intervals due to uneven pressure depletion, uneven gas and water mobility. These production risks and uncertainties shall be evaluated for ensuring the predicted life-cycle production performance of the designed commingles production wells. Minimization of these risks could involve developing of a pressure drawdown management plan, the optimization of injection fluid conformance control and the prediction of reservoir pressure change. The resulting pressure drawdown plan may then generate a requirement for individual down-hole flow control at each commingled sands. Accordingly, the smart completion comprises of inflow control devices such as passive ICD and/or active ICV with or without down-hole pressure and inflow monitoring devices namely, PDG or DTS installation can then be adequately designed. This paper is to illustrate a production integrated smart well completion design process starting from reservoir drainage and injection points selection, the determination of well reservoir contact trajectory, the production evaluation and risk analysis, and to the selection and application of smart completion devices. The case of a deepwater reservoir field development smart well completion design was used to demonstrate the viability of this integrated engineering approach. This approach is a partial effort to achieve effective field development by lowering the overall field development cost and maximizing the oil and gas recovery. The presented reservoir engineering workflows and completion design methodologies is to constitute a new smart well completion benchmark for well design and production optimization and serve as an engineering guide for optimizing the well construction cost in Malaysia.

Smart Horizontal Well Drilling and Completion for Effective Development of Thin Oil-Rim Reservoirs in Malaysia

For thin oil-rim reservoirs, well placement, well type, well path and the completion methods shall be evaluated with close integration of key reservoir and production engineering considerations. This involves maximizing reservoir fluid contact and drainage, optimizing the well productivity, and optimizing well life-cycle production profile along the wellbore. Field implementation cases in Malaysia have been shown that this integrated approach to design and drill horizontal wells can significantly minimize the well count, enhance the well performance, and improve the ultimate recovery per well in thin oil-rim reservoirs with varying reservoir complexity and uncertainties. 45 horizontal wells were progressively drilled and all completed with ICD in a relatively flat thin oil-rim reservoir offshore in peninsular Malaysia. In this successful oil development, well path between the GOC and OWC was optimized to delay the water breakthrough and reduce the decline trend in different reservoir sectors with varying horizontal well length up to > 2,000 m. Good performance of the ICD was confirmed by PLT surveys and by tracer effluents evaluation with different type of tracers implemented in various sections of the horizontal well completion. For a low pressure thinner oil-rim reservoir in offshore Sabah, horizontal wells were drilled with ICV completed in the gas cap. This smart well design enables having in-situ gas lift operation during the initial oil production, and progressively changing to the planned gas-cap blow-down operation for maximizing the overall hydrocarbon recovery. In another oil-rim reservoir, a long horizontal wellbore with ICD design was completed with dual-strings to further optimize drawdown pressure distribution along the long wellbore and improve oil drainage and oil recovery.

An Integrated case study from Seismic to Simulation through Geostatistical Inversion

The field is located in the Persian Gulf and has been producing for the last 30 years with a strong natural aquifer support. The clastic reservoir exhibits highly heterogeneous permeability combined with shale streaks and therefore presents complex flow behavior. This paper describes the iterative seismic to simulation workflow followed to create a fine scale reservoir static and dynamic simulation model consistent with all available engineering, geologic and geophysical data. The process involved integration of static and dynamic modelling workflows. History matching the production data indicates locations with incorrect information in the static model, which can be corrected and re-exported for the dynamic model in very short time. The integration of static and dynamic modelling is seen as essential for the further commercial development of the field. A comprehensive integrated study was conducted starting from petrophysical log evaluation and resulting in fine scale reservoir models on a geo-cellular grid. To reduce the uncertainty, a model was created using a geostatistical inversion technique which honoured both geologic and seismic information. The use of high resolution geostatistical inversion provided good and reliable estimates of porosity and lithology away from the wells. The porosity and lithology models were further tested on history matching 92 producing wells for 30 years. The quick match resulted in less uncertainty of porosity and higher confidence in the prediction models. The history matched model is predicted further to define different potential development scenarios. It is now 3 years since all the data used in the study was acquired and the current field production matches with the model prediction. The models created using geostatistical inversion proved to be robust and predictive for the field development.