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Sulaksana, A.. (Sabah Shell Petroleum Company, Limited) | Cheers, M.. (Sabah Shell Petroleum Company, Limited) | Dols, H.. (Sabah Shell Petroleum Company, Limited) | Yap, V.. (Sabah Shell Petroleum Company, Limited)
Abstract The field under the study is located offshore Sabah, Malaysia. It is a mature field with 55 wells, and has been on production for more than 30 years. Despite continual efforts to improve the reservoir description and subsequent placement of infill wells, the average ultimate recoveries of the production wells in the field remain stubbornly low. In terms of reservoir continuity, the field represents the "Perfect Storm" in that the reservoir sands are thin, interbedded with shales, and appear to be extensively faulted. Furthermore, the problems of characterization are exacerbated by poor seismic imaging over the crest of the field, and an unreliable well-to-well correlation due to a lack of clearly distinguishable marine shales. As a structural modelling subject, the field represents a complex problem which exposes the limitations of pillar gridding for geo-cellular model construction. The recovery factor for the field stands at 9% indicating significant recoverable reserves remain, driving the need to better understand the reservoir geology and how this relates to production behavior. A novel reservoir modelling approach was required which would retain enough granularity in the model grid to represent the complex structural & stratigraphic compartmentalization, but without the penalty of excessive simulation run times. The complex stacked stratigraphy and fluid distribution was delineated through integrated analysis of the seismic (in so much as this was possible), the fluid contacts at the wells and the reservoir pressure data, resulting in an initial model of 835 discrete structural/stratigraphic compartments. Within this structural framework, stochastic modelling of net sand facies was used to create the heterogeneous sands and shale which form the fabric of the reservoir which reproduced the baffled connectivity required to emulate the severe pressure depletion and subsequent recovery that is commonly seen. Oil in place volume estimates were re-confirmed by the history matched dynamic model via experimental design, which also resulting multiple best match cases. The multiple static and dynamic models permitted a consistent approach to identifying infill drilling targets and assessing redevelopment feasibility, and the associated uncertainties. With the focus on regions that are currently inaccessible from the existing infrastructure, the study ultimately recommended the location of two new drilling platforms to provide optimal access to the remaining oil. The novel reservoir modeling, which includes the integration of seismic, fluid contact and pressure data to better define reservoir correlation and compartmentalization, was successfully applied to quantify the size of the prize of this highly compartmentalized reservoir. The experimental design approach was then instrumental in managing the principle uncertainties in a consistent way to develop a range history matched subsurface models used to identify future development options.
Nwonodi, Chike (Shell Petroleum Development Company Limited) | Onwuchekwa, Chukwuma (Shell Petroleum Development Company Limited) | Campero, Manuel (Shell Petroleum Development Company Limited) | Eke, Kenneth (Shell Petroleum Development Company Limited) | Madu, John (Shell Petroleum Development Company Limited) | Adenaiye, Niyi (Shell Petroleum Development Company Limited) | Umoh, Magdalene (Shell Petroleum Development Company Limited) | Oluwaseyi, Adekoya (Shell Petroleum Development Company Limited) | Igogo, Arit (Shell Petroleum Development Company Limited) | Anosike, Ebere (Shell Petroleum Development Company Limited)
Abstract 3D reservoir modelling of stacked reservoirs is often difficult, due to interplay of various uncertainties related to heterogeneity of the sand units modelled together within the stack. In the past, reservoir simulation had been used to model stacked reservoirs for commingled wells with varying predictive capacity. Typically, dynamic models use conventional approach in assessing uncertainties, involving discrete sensitivity of uncertain parameters and often lack robust subsurface uncertainty management. Additionally, handling dynamic cross flow issues by modelling of control valves in such cases presents production allocation challenges. Huge man-hours are needed for several run times, without achieving relatively good history-match and predictive capacity. As a result, only base-case history-matched model is developed; thus robust assessment of impact of sub-surface uncertainties on the predictions is usually inadequate. This paper details a multidisciplinary approach adopted in integrated dynamic modelling of stacked reservoirs in a Niger Delta field using experimental design methodology. The phased development was aimed at full-scale reservoir development of multi-zone and commingled wells. Instead of single average for each sand unit, facies-dependent kvkh values were required to adequately capture reservoir heterogeneity for the complex depositional environment. Field-wide history-matches for 23 reservoir blocks and over 70 individual conduit matches were done to calibrate the simulation models. Model robustness was conducted through blind-testing and calibration with carbon-oxygen and openhole log data from newly drilled wells. From the model predictions, recoveries for existing conduits from simulation models were benchmarked against Decline Curve Analyses and the results compared closely. Response Surface Models were used in selecting best history-matched realizations. 1P, 2P and 3P models were selected from the ultimate recovery probability distribution curves of these history-matched realizations. The modelling of interval control valves settings within the stacked models and use of smart well routines assisted evaluation of recoveries from planned commingled wells.
Abstract A multi-discipline integrated full field review (FFR) was conducted for Betty field, offshore Malaysia, in order to build a set of 3D predictive numerical models from multi-scale geological, seismic, petrophysical, reservoir and production engineering data.This study reassessed field stock tank oil initially in-place (STOIIP) volumes and remaining reserves, determined infill drilling potential and identified opportunities to improve both short-term and long-term field performance.Betty field comprises of multiple stacked, laterally continuous, vertically heterogeneous reservoirs. Some reservoirs have experienced relatively high recovery factor (RF) to date (i.e., >69%), while others have underperformed (RF<15%). One of the priorities of this study was to resolve these anomalies. Detailed evaluation of the core identified five predominant lithofacies in each reservoir. Horizons, which were interpreted from 3D seismic and tied to the well logs, formed the framework for the static model structure. The neural network-based lithofacies analysis for all the wells enabled distribution of the lithofacies data in a 3D geocellular model, which significantly improved the accuracy of the rock property distribution in the reservoirs. Vertical trend functions based on electrofacies logs were input to control the facies content of each layer, and the resultant facies model was used to control the porosity distribution using Sequential Gaussian Simulation (SGS) throughout a fine grid (11 million cells, 1-ft. layers). SGS was also used as co-simulation for permeability, coupled with vertical and horizontal variograms honoring the appropriate facies proportion in each layer.The static model was upscaled (to 600,000 cells, 6-ft layers) after correlating the lithofacies from well to well with over 2800 geological markers to preserve vertical heterogeneity. The initial saturation distribution determined from gravity-capillary equilibrium and a single J-function anchored at the base of the water-oil transition zone, was essentially corroborated by petrophysical analyses.Representative drainage/imbibition relative permeability curves were established from the available data and deployed for dynamic modeling. As a testament to the integrity of the data, the technical interpretation and overall approach that was used, it was found that almost 75% of the 66 historical completions were essentially matched for 27 years of history after making trial runs of the dynamic model without any adjustments to the static reservoir description. Betty Field and Its Uncertainties Betty field is located 40 km offshore Miri in the Baram Delta area of Sarawak, Malaysia. Discovered in 1968, its first production was in 1978.The average water depth is 225 ft. There are 22 stacked reservoirs of varying size and thickness between depths of 7,200 ft sub-sea (ss) and 9650 ft ss. The field is operated by 1 platform that contains 24 production wells with 48 tubing strings.There have been 66 historical completions in the field over a period of 27 years. Recent advancements in modeling technology utilized in multi-disciplinary field studies have provided opportunities to increase recoverable reserves and reduce uncertainties, which leads to better planning for future field developments[1–4]. The primary objectives of this FFR were to (a) re-assess field STOIIP volumes and remaining reserves, (b) estimate infill drilling potential, and (c) identify rapid performance improvement opportunities by resolving near-wellbore issues.In Betty field, some producing reservoirs have experienced high recovery efficiency, while in others the recovery efficiency has remained relatively low.One goal of this study was to investigate and resolve these recovery efficiency anomalies and to identify areas where recovery could be improved.Another goal was to refine estimates of both the initial in-place and ultimate recoverable hydrocarbon volumes by reducing reservoir uncertainties through reconciliation of all available multi-disciplinary multi-scale data.
Abstract Objectives/Scope: This paper showcases how an integrated static and dynamic modelling workflow was applied to cover a wide range of subsurface uncertainties in order to generate a more robust range of forecasts. These forecasts are in the process of being used to optimize the ongoing development plan for a marginal offshore field in South East Asia. Due to an anticipated lack of aquifer support, the initial phase of development was based on flank and crestal producers with peripheral water injection to deplete Miocene-aged reservoirs within a fluvial-dominated coastal plain to delta environment where the predominant lithologies are clean sandstones and sandy to muddy heterolithics. The results from the early development wells indicated a more complicated reservoir than has been previously interpreted, as evidenced from well results, formation pressure data, flowing /shut-in pressure trends and early production data across the field. Methods, Procedures, Process: For a field with an increasingly complex geological and dynamic background, use of an integrated workflow covering both the static and dynamic parameters allows for seamless updating and ensures continuity between geological and dynamic models when assessing the field uncertainty. This is achieved by predefining value sets and their uncertainty ranges and employing an experimental design technique. The information for each realization is used to generate a proxy model, which is then used to interpolate an objective function between runs and thus manage uncertainty. The proxy model provides a quantified distribution for each combination of input parameters in the reservoir simulation model with approximate results for any set of modifier values then generated. The methodology provides a better understanding of subsurface risk assessment in conjunction with assisted history matching, thereby optimizing the development plan. Using an integrated approach allows the geological model to be current at all times during the drilling campaign and potentially allows for optimization during the campaign. Results, Observations, Conclusions: This case study presents the results of the Integrated Uncertainty Analysis Workflow which resulted in sets of realizations of multiple scenario-based history matched results, whilst maintaining geological consistency between the static and dynamic models for reservoir behaviour predictions. The oil production and recovery ranges were then determined from the probability and cumulative density function resulting from these multiple realizations. The effectiveness and challenges encountered when applying the workflow in a real field example will also be discussed in this paper. Additional Information: Risk can be quantified and uncertainty managed within both static and dynamic models through an integrated workflow that covers the entire reservoir modelling process from seismic interpretation through to simulation and reservoir predictions. This implies significant cost savings, as this workflow provides results quicker and generates a comprehensive quantification and management of uncertainty within the reservoir modelling workflow. The geologically-consistent models enabled the delivery of an optimized reservoir model for waterflood management; a probabilistic approach to production forecasting; and crucial input into maximizing recovery and optimizing the field's economics. Such a workflow has the potential to be updated in near real time, and executed in a time space in line of well construction – thus optimization of well placement based on changes in the underlying geology, whilst maximize recovery are potentially possible.
Tyrie, Jeb (Bridge Petroleum) | Mulcahy, Matt (Bridge Petroleum) | Leask, Robbie (Bridge Petroleum) | Wahid, Fazrie (Bridge Petroleum) | Arogundade, Olamide (Schlumberger) | Khattak, Iftikhar (Schlumberger) | Apena, Gani (Schlumberger) | Samy, Mohammed (Schlumberger) | Sagar, Rajiv (Schlumberger) | Xia, Tianxiang (TRACS International) | Nyadu, Kofi (WorleyParsons, Advision) | Maizeret, Pierre-David (Schlumberger)
This paper describes the proposed re-development of the Galapagos Field, comprising the abandoned NW Hutton field and the Darwin discovery (Block 211/27 UKCS) which forms a southerly extension. The paper covers the initial concept and analytical evaluation, the static uncertainty model build, the dynamic model history-match, the iterations between static and dynamic modelling, the development subsea and well locations, the optimisation workflow of the advanced Flow Control Valve (FCV) completions in both producers and injectors and the facilities constraints.
The redevelopment plan involved several multi-disciplinary teams. 20 years of production data from 52 wells were analysed to identify the production behaviour and confirm the significant target that provided the basis for the development concept selection. The full Brent sequence compartmentalised stochastic static model was based on reprocessed seismic plus 14 exploration and appraisal wells. Streamlines, uncertainty sensitivities and mostly good detective work honed a history match to RFT, BHP, PLT and oil and water production. P50, P90/P10 models were selected and over 100 FCVs optimised to deliver the profiles against an identified FSPO facilities’ constraints.
Over 1,000 static models were delivered consisting of sheet sands, incised valleys and channels in heterolithic facies overprinted by a depth trend with appropriate uncertainty ranges. The high well count gave a tight STOIIP probabilistic range of 790/883/937 million stb. The early RFTs illustrated extreme differential depletion between Brent zones and subzones of the Ness. To history-match these the dynamic model retained the static model definition in the Upper Ness to capture the thin but extensive shales. The early 18-month depletion and the late steady production-injection phases were simulated separately in prediction mode and matched the Production Analysis estimated ‘future’ production giving confidence to the history matched model. The initial concept development of 4 subsea-centres, to cover the large field area, with an injector in each compartment proved a robust selection. The horizontal wells increase PI where needed and mitigate internal faulting. The optimisation of the FCVs significantly increased oil production from all zones and drastically reduced water injection and production so that the identified FPSO modifications were relatively modest. The final First Stage Field Development Plan consists of 11 producers and 6 injectors across developed and undeveloped areas confirmed robust P50 reserves of 84 million boe.
Robust concept selection allowed for early identification of production units so that constraints and modifications could be accounted for within the economic model.
The Galapagos field re-development plan is an excellent example of how detailed static and fully history matched dynamic models can lay the foundations for new technology like the optimisation of the FCVs to access bypassed reserves using significantly smaller production units with reduced requirements for power, compression, gas lift, pumping pressure, injection and production. In short, they shrank the facilities.