The Burgan Sand reservoir is the largest and most prolific reservoir in the Greater Burgan field in Kuwait. As development of this reservoir proceeds, greater attention is being paid to the more heterogeneous upper part of the reservoir, the Upper Burgan Sands, which contain significant, multi-billion barrel, resources. A conceptual Field Development Plan was developed to enable an assessment of performance under waterflooding. Due to the complexity of the Upper Burgan Sands and the very large areal extent of the Greater Burgan field, it was considered impractical to model the reservoir in appropriate detail in the full field simulation model. An alternative modeling approach was adopted. This involved developing a number of part field geological models which were subsequently used to develop high resolution dynamic simulation models. This paper describes the challenges of choosing an appropriate range of part field models that capture variation in geological characteristics and of hydrocarbon properties, conditioning these models to available dynamic data and accounting for the historical production. Particular attention is paid to three problems. First, to rank the models in terms of key features that would be expected to govern reservoir performance. These included internal heterogeneity, average reservoir quality, connectivity to the underlying Middle Burgan Sands and variation in fluid properties. Second, to assign the selected models to represent different parts of the reservoir. Third, to ensure that historical performance, including water influx inferred from surveillance data, is accounted for in predicting future performance. The estimates were determined by a hybrid formulation, combining an analytic and simulation approach, suited for rapid computations and multi-scenario generation. Simulation results of water-cut versus recovery have been integrated with standard analytic expressions for fully developed pattern waterfloods. Scenarios investigated include the determination of ultimate recovery, a phased out recovery according to production and sweep of lower intervals, sensitivities to drilling rate, and a waterflooding scheme prioritized on area production potential. Based on the results from the part field models, performance estimates of the Upper Burgan Sands reservoir have been made. Volumes forecasts and associated well numbers have been predicted. A new reservoir simulation and analytical formulation has been developed to enable rapid predictions of waterflood scenarios. The hybrid formulation has proved to be significantly faster, resulting in a much quicker turnaround time compared to a traditional simulation study.
Increasing global demand for gas has led to a larger focus on production from deep gas HP/HT reservoirs. The case study presents an offshore field under current development with several vertical wells that confirmed varying reservoir extent, successful stimulation, and productivity. The target formation is a naturally fractured HP/HT low permeability clastic reservoir with lateral and vertical reservoir quality variations. Measured stress rotation within the structure and large minimum stress variations across different reservoir intervals support the presence of a complex stress environment where the development of a predictive 1D Mechanical Earth Model (1DMEM) becomes challenging. The combination of a variable stress dependent permeability within an altered stress field introduces uncertainty in the estimation of fracture geometry and hurdles its optimization. The paper presents a multi-frac completion strategy to be implemented in newly drilled development wells by utilizing state-of-the-art hydraulic fracturing optimization workflows driven by an integrated multidisciplinary approach. Introduction The offshore field presented here is located west of Abu Dhabi city. The subsurface structure was first identified in 1966 and is part of a regional N-S extending structural trend with other nearby fields. The targeted formation is a deep naturally fractured tight gas clastic reservoir that presents lateral and vertical reservoir quality variations. It possesses three different reservoir units (A, B and C) with diverse petrophysical and mechanical properties. The new development considers cased and cemented s-shaped wells with a dedicated 4 ½” frac string to pump stimulation treatments at high rates. The “plug and perf” completion methodology was selected since this allows flexibility given the initial uncertainty in the hydraulic fracture growth and to properly evaluate intervals production performance.
Summary Brownfields in this paper are defined as mature fields where production declined to less than 35–40% of the plateau rate and where primary and secondary reserves have been largely depleted. Big data, high field complexity after a long production history, and slim economic margins are typical brownfield challenges. In the exploration-and-production (E&P) industry, “sequential” field-evaluation approaches (first “static,” then “dynamic”), have proved successful for greenfield development, but often do not achieve satisfying results for brownfields. This paper presents a new work flow for brownfield re-evaluation and rejuvenation. The “reversed” geo-dynamic field modeling (GDFM) rearranges existing elements of reservoir evaluation to obtain a purpose-driven, deterministic reservoir model, which can be quickly translated into development scenarios. The GDFM work flow is novel because (1) it turns upside down the discipline-driven sequential work flow (i.e., starts with the history match) and (2) it uses dynamic data as input to calibrate seismic (re-) interpretation that acts as a main integration step. It combines all available data already during horizon and fault mapping. Field diagnosis, flow-unit identification, well-test reanalysis, and petrophysical and geological interpretations are all combined in a cross-discipline interaction to guarantee data consistency. This directly ensures a fully integrated, “geo-dynamic” model that forms the basis for reservoir modeling. The full dynamic/static data coupling at an early stage is the main strength of the GDFM. It reduces the model complexity, and narrows the uncertainties. Project-execution time is considerably shortened by avoidance of the characteristic full-cycle loop iterations of the sequential approaches. A brownfield example illustrates the benefits of GDFM: a consistent history match with high model accuracy and confidence. In the field example, the GDFM work flow has facilitated a turnover at only 70% of the original time budget. The ongoing drilling has confirmed model validity (“attic oil” predictions), thus further postponing the economic limit of the brownfield.
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