Enhanced Geostatistical Mapping of Reservoir Fluids

Perez, Godofredo; Chopra, Anil K.; Severson, C.D.

doi:10.2118/29964-ms

Cited by 2 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yarus and Chambers 6 present papers that describe several applications of geostatistics for reservoir description. Most recent geostatistics work include methodologies to integrate log and core data, 7 geologic models, [8][9][10] production histories and surveillance data, [11][12][13] well-tests, [14][15] and 3D seismic data. [16][17] Fractals are one of the first geostatistical tools used to characterize spatial heterogeneity of reservoir properties.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Fractal Models To Describe Reservoir Heterogeneity and Performance

Perez

Chopra

1997

SPE Formation Evaluation

View full text Add to dashboard Cite

Our work uses fractals to characterize the spatial correlation structures of porosity and permeability of vertical and horizontal logs in a braided-fluvial sandstone reservoir. The data comprise cores and logs of adjacent vertical wells and logs of horizontal wells. Smallscale realizations of interwell heterogeneity were generated with successive random additions (SRA) technique for new observed fractal models. A deterministic description is also developed. Effects of fractal and deterministic reservoir heterogeneity on reservoir performance were studied for waterflood and water-alternating-gas (WAG) injection processes. The effects of scale up on spatial correlation of porosity was investigated.The fractal model of fractional Brownian motion (FBM) in the horizontal direction, having same intermittency exponent as for fractional Gaussian noise (FGN) in the vertical direction, is not supported by log observations in horizontal wells. Fractal character of core and log data of vertical wells is similar. The incremental WAG recovery response compared to waterflooding response is more sensitive to reservoir heterogeneity. Scale-up experiments indicate that spatial correlation structure of reservoir properties may be different at different scales.Results of this paper will be useful for evaluation of infill drilling, and design, selection, and optimization of an EOR process. The proposed techniques also provide a framework to quantify uncertainty in reservoir performance.

show abstract

Section: Introductionmentioning

confidence: 99%

Evaluation of Fractal Models To Describe Reservoir Heterogeneity and Performance

Perez

Chopra

1997

SPE Formation Evaluation

View full text Add to dashboard Cite

show abstract

Application of Neural Networks to Modeling Fluid Contacts in Prudhoe Bay

Panda¹,

Pérez²,

Chopra³

1996

SPE Journal

View full text Add to dashboard Cite

Modeling the dynamic behavior of fluid movement in an oil reservoir is complicated because of non-linear interactions between the reservoir heterogeneity and fluid flow. Simple regression, geostatistical and numerical simulation techniques have been used in the past to model fluid movement with various degrees of success. Most of these methods, however, suffer from common drawbacks that they are time consuming and difficult to automate. This paper presents a new method based on artificial neural networks (ANNs) to model dynamic gas-oil contacts in the Prudhoe Bay reservoir. This method is fast, efficient, and highly automated and requires minimum user intervention. The proposed method uses oil, gas, and water production, perforation history, permeability, sand and shale distribution, and surveillance data at surrounding wells as input to an ANN to predict the fluid distribution at a target well. A two-step method is developed to design an ANN. The first step trains the network using previously measured data as input and output, and thus establishing the internal rules of the network. The second step uses the trained network to estimate the fluid distribution at target wells. Results show that the ANN method can predict fluid distribution at target wells more accurately and consistently than the conventional regression-based methods. Introduction The Prudhoe Bay field on the north coast of Alaska is the largest oil field in the North America, with total estimated original oil in place (OOIP) of about 22 billion barrels. The field is overlain by a large gas cap and a major portion of the field is being produced by gravity drainage. Waterflood operations are confined to the down-structure and peripheral areas of the field. Geographically, Prudhoe Bay can be roughly divided into two regions based on the development strategy and current recovery mechanisms: gravity drainage and water and miscible gas flooding (Fig. 1). This paper deals only with the gravity drainage area. Characterizing fluid movement in a reservoir is a critical engineering task because it forts key elements for field development, reservoir management, and predicting reservoir performance for different mechanisms. The benefits of such a characterization include reduction of gas and water handling costs, selection of completion and re-completion intervals, and development of better reservoir simulation models. In Prudhoe Bay, the interactions between different drive mechanisms, such as gravity drainage, gas cap expansion, waterflooding, miscible displacement, and the reservoir architecture and heterogeneities (shales, faults, and fractures) result in complex gas and water movement through time. Shales of varying sizes, which may not be continuous between wells, have a significant effect on the gas movement.

show abstract

Enhanced Geostatistical Mapping of Reservoir Fluids

Cited by 2 publications

References 6 publications

Evaluation of Fractal Models To Describe Reservoir Heterogeneity and Performance

Evaluation of Fractal Models To Describe Reservoir Heterogeneity and Performance

Application of Neural Networks to Modeling Fluid Contacts in Prudhoe Bay

Contact Info

Product

Resources

About