Recovery from an oil zone underlying a gas cap, overlying an aquifer, or sandwiched between gas and water can be improved by repressing the coning problem through horizontal-well drainage. Literature methods to predict coning behavior are limited to steady-state flow conditions and determination of the critical rate. The results in this paper are based on new semianalytical solutions for time development of a gas or water cone and of simultaneous gas and water cones in an anisotropic infinite reservoir with a horizontal well placed in the oil column. The solutions are derived by a moving-boundary method with gravity equilibrium assumed in the cones. For the gas-cone case, the semianalytical results are presented as a single dimensionless curve (time to breakthrough vs. rate) and as a simple analytical expression for dimensionless rates > l!J. For the simultaneous gas-and water-cone case, the results are given in two dimensionless sets of curves: one for the optimum vertical well placement and one for the corresponding time to breakthrough, both as functions of rate with the density contrast as a parameter. The validity of the results has been extensively tested by a general numerical simulation model. Sample calculations with reservoir data from the Troll field and comparison with test data from the Helder field demonstrate how the theory can be used to estimate the time to cone breakthrough and its sensitivity to the uncertainties in reservoir parameters.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThe Yufutsu field is a fractured granite, gas condensate reservoir, located on Hokkaido island, in the north of Japan. There is minimal primary porosity and secondary porosity consists of fractures. Fractures are categorised into four types according to size (mega, major, minor and halo fractures). Mega fractures govern the flow, while micro fractures govern gas storage. Gas migrated into the reservoir after the fractures were formed.

A case study is presented from an oil field in the Sultanate of Oman, operated by Petroleum Development Oman. To date the field has seen limited production under natural depletion (~1% of STOIIP produced). Most of the oil is located in 20–25 m thick oil rims with large gas caps in the reservoirs of the Gharif and Al Khlata formations. A Field Study was kicked off in PDO in 2004, triggered by the results of recent appraisal wells and has substantiated significant additional field potential. This paper extends previous work [Ref 1] in which we set out the use of Experimental Design for subsurface uncertainties analysis and development options evaluation for an oil field in the Sultanate of Oman. Here, we demonstrate for the first time how the multi-scenario ED modeling results can be taken one step further in Field Development optimisation. We introduce the concept of "Well Maturity Index" which quantifies the impact of subsurface uncertainties on EUR for each well in the selected development. The Well Maturity Index was used to guide decisions on the phasing of development and appraisal drilling and to help formulate a data acquisition programme for the field. This approach has resulted in an auditable and quantified decision-based plan for maximizing NPV and minimising risk, and adds significant value to the development planning process. This work also provides a natural stepping stone for a quantifiable Value of Information analysis, and scenario planning based on appraisal outcomes. The methodology used is general and should find application in many field development studies.. Introduction The Case Study field is located in onshore North Oman and is operated by Petroleum Development Oman. The relatively light oil is located in stacked oil rims beneath large gas caps in four different reservoirs (AK, LG, MG and UG). The field is associated with a mainly dip-closed, low relief faulted anticline, with its crest at a depth of 3000 m. Intervening shales and tight limestones act as intra-reservoir seals, and the UG, MG and LG/AK formations have separate fluid contacts. To date the field has seen limited production under natural depletion with three phase flow to remote processing facilities. A schematic of the field is given in Figure 1. A Field Development Plan (FDP) was derived after an extensive multi-scenario modeling approach using Experimental Design [Ref 1], followed by deterministic optimisation and by testing of the development against subsurface uncertainty. Key elements of the FDP are:Initial drilling to fill existing and committed facilities with low GOR oil by developing the AK oil rim with horizontal wells, coupled with appraisal of the overlying LG MG and UG reservoirs.Installation of full field facilities. Gas separation and compression with gas and oil/water routed to dedicated processing facilities. The increased capacity to be filled with production from potentially higher GOR LG and UG reservoirs.Final gas cap blow down will be carried out at the end of economic oil production. The multi-scenario ED results have also created a wealth of useful data, that can be further explored. A more detailed quantification of the impact of subsurface uncertainties can be pursued for the final development, on a well by well basis. This paper describes how this has further refined the phasing of development and appraisal drilling and how this has tailored the data acquisition programme to be more specific to each of the wells. Also, ranges for key surface facility design parameters were defined and were used for further optimisation the design specifications. Experimental Design Results Experimental Design is a mathematical technique which aims at representative sampling of the full parameter space with a relatively small number of parameter combinations. It is now widely recognized in the oil industry that ED provides a robust and time efficient way of handling multi-factorial problems such as volumetric calculations, history matching and development screening [Ref 1- 8].

Recent years have seen a growing trend towards "right sizing" in the oil business. This has lead to improved efficiency and greater technical challenges. One of the off spins of this process is the current trend to limit up front investments in processing capacity, by designing it to the minimum production stream that will yield the maximum profit. This paper addresses these challenges and describes a process of combining 3D simulation results and Linear Programming (LP) to optimise asset value under numerous production and drilling constraints. The subsequent application maximises post tax NPV, and can be used for optimising drilling schedules, IOR projects, and process upgrades simultaneously. A newly developed well production rate formulation, including influence functions, allows for large time savings and a robust solution. This formulation also allows choking of wells without loss of reserves. This work differs from preceding studies by allowing significant reduction in data requirements, and incorporating an iterative solution between LP and reservoir simulation. This also opens the possibility to verify the accuracy of the optimal solution. The number of simulation runs required is reduced by a factor 40 compared to conventional LP formulations. The Ekofisk area in the North Sea is used as an example of the application. This area comprises several fields and platforms and is currently facing huge redevelopment challenges with new platforms and decommissioning of existing platforms. Furthermore infill drilling of more than 50 wells is premised and there exists IOR opportunities. All the redevelopment has to respect the constraints for oil and gas production and the rig availability. Results from the application is an optimised production strategy/drilling schedule for all fields in the area, leading to a significant increase in project value. The solution also resulted in a longer plateau production and higher ultimate reserves, through the drilling schedule optimisation. The proposed method offers a new method of LP formulation incorporating both influence functions, well scheduling effects and choking of wells, which allow iterative solutions. Introduction The most common method of field development evaluation is to investigate several basic plans which are based on experience and judgement. Based on development screening one concept is chosen for detailed study. In instances of combined redevelopment with a large number of drilling opportunities, multiple reservoir tie-in decisions, IOR evaluations, and processing/transportation limitations, a complete optimisation study is required. Initially the scope of such a study may appear prohibitive. This paper provides a formulation of optimisation and reservoir simulation which is manageable with a minimum use of time and resources. Linear Programming (LP) has previously been used to address situations as described above. Linear Programming is a term related to the systematic solution of a set of linear equations in order to optimise a given objective function. The formulation of a field development study into a LP problem presents many challenges, both with respect to linearisation of the problem and the size of the problem. In addition most real development cases will have to solved by Mixed Integer Programming (MIP), in order to allow predefined variables to assume the value of 0 or 1. These types of variables can be viewed as yes/no flags and are a key component of forming a development strategy. P. 497^

fax 01-972-952-9435. AbstractExperimental Design has been used to screen a wide range of potential development options available for an oil rim reservoir, to examine the effect on the development of dependencies between surface and subsurface parameters, and to test the robustness of the optimised project against subsurface uncertainty. The ED algorithm used in this study enabled parameter screening and the parameter interactions to be performed in one step. Development screening was carried out by a process of eliminating options which impacted negatively on recovery and NPV. The interaction between surface and subsurface parameters was assessed and confirmed the robustness of the selected development. Final optimisation of the chosen development was carried out deterministically. Screening of the optimised development against subsurface uncertainty was used to derive a range of production forecasts, to guide the phasing of the development, and to formulate data gathering and appraisal plans.