Production Forecasting for Gas Fields With Multiple Reservoirs

Summary

A new method to predict field performance for gas fields predict fieldperformance for gas fields consisting of small reservoirs of pointbar originwas developed, pointbar origin was developed, implemented, and tested.Conventional geological and reservoir engineering modeling of this type offield is difficult and usually gives erroneous results. This paper describesthe evaluation and use of exploration- and appraisal-well data as input for astochastic geological model and the use of output from that model as input fora semianalytical reservoir performance model. The semianalytical model includesproduction constraints for well, platform, and field conditions. Exampleapplications of the stochastic geological model and the semianalytical modelare also presented.

Introduction

Oil and gas fields that consist of sand deposits of fluvial origin may haveextremely complicated or even chaotic structures. Sand lenses are discontinuousand cannot be correlated from well to well. Fields can consist of thousands ofseparate reservoirs. The thickness of the pay intervals can be very large(e.g., up to 1000 m). Between pay intervals, reservoirs may be water saturatedand fluid properties may change rapidly from reservoir to reservoir. Fig. 1illustrates the complexity of this type of field.

A geological evaluation should estimate not only in-place reserves, but alsoreserves penetrated by wells. The evaluation must penetrated by wells. Theevaluation must also consider the variations of reserves at all levels becauseaveraging of volumes and properties gives overly optimistic field fore-casts.properties gives overly optimistic field fore-casts. Variations usually lead tobottlenecks and surprises.

Stochastic geological models have been presented in the literature.Haldorsen and presented in the literature. Haldorsen and Lake developed a 2Dmodel that distributes shale intervals stochastically. Augedal et al. developeda 3D model that distributes sand bodies that are parallelpipeds. Both modelsassume a constant net/gross ratio over the field.

The model presented here is a 3D model designed to describe point-bardeposits in a mud-rich environment. The analytical model is designed to handledata from a large number of reservoirs (greater than 1,000) with complexproduction controls. The models presented in the literature do not take intoaccount the complex production procedures and reservoir management program.

Geology of Meandering River Systems

Meandering river systems are normally formed in areas of relatively lowslope. Heavy vegetation and cohesive flood-plain deposits make rivers morestable and favor development of meandering rather than braided river systems.The flow pattern of meandering systems causes erosion at the outer bank anddeposition at the inner bank. Thus, the position of the river changes and pointbars form (Fig. 2). point bars form (Fig. 2). Each individual point bar is afining-up sequence, with high-energy deposits (gravel and sand) at the bottomand low-energy deposits (such as silt and shale) at the top. Fig. 3 shows atypical gamma ray response from such a sequence. The vertical shift from sandto shale intervals in the wells reflects the shifting nature of rivers owing toavulsion of the meander-belt. In sand-rich meandrous environments with lowsubsidence rates, meander-belt deposits commonly develop extensive sand bodiesthat are parallel to the overall transport direction of parallel to the overalltransport direction of the river, where the continuity is very good.

In a more mud-rich environment with a high subsidence rate, isolated pointbars form. Some amalgamated sequences may occur, but the continuity is verypoor. Fig. 1 shows single and amalgamated sequences. The water-saturatedintervals in Well B clearly demonstrate the lack of continuity for bothsequences.