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.

Geostatistics is an increasingly important tool for developing an integrated reservoir description. Though Applied Geostatistics for Reservoir Characterization is written to illustrate the importance of geostatistics in improving the reservoir characterization process, it does not require any prior knowledge of statistics or advanced mathematics. Emphasis is placed on intuitive understanding of procedure rather than on mathematical details. Each chapter has an associated appendix in which additional mathematical details are provided. Several numerical and field examples, as well as a large number of illustrations, are provided to explain the strengths and weaknesses of different methods. Media Resources (http://go.spe.org/AGRCmedia)

this article begins on the next page F F JCPT90-01-09 RESERVOIR PERFORMANCE AND OPTIMIZATION A simplified method to predict over-all production performance GODOFREDO PEREZ and BALMOHAN G. KELKAR The University of Tulsa ABSTRACT Th@ paper presents a simple method to predict the over-all produc-tion performance of oil and gas systems. Conventional methods usually require time consuming graphical approaches. The pro-posed method is based on a simple mathematical algorithm that Provides fast and accurate means to estimate the over-all perform-ance of a production system. In addition, the proposed method has been designed to search for unstable production conditions that might exist in the system. The method has been used to develop a computer model representing a system consisting of several production components. The production components considered in the computer model are.-reservoir, perforations, gravel pack, tubing, subsurface safety valve, wellhead choke, surface pipeline and separator. In addi-tion, a gas lift system is also included.Practical applications of the model are presented, and these in-clude.-sensitivity analysis of the performance of an oil produc-tion system and evaluation of unstable conditions in a gas condensate system. Results from this model are compared and agree closely with several cases presented in the literature. Introduction Over-all production performance is the flow rate that a system is able to sustain under the restrictions imposed by the produc-tion components. Physical restrictions of production systems can be divided into several components such that the pressure losses across each component are evaluated independently. Correct de-termination of the performance of a production system depends ultimately on the accuracy of the pressure loss predictions across each component of the system. Research effons in the last few years have resulted in several methods and correlations capable of accurately predicting pressure losses across different produc-tion components for a wide range of operating conditions. These include, correlations to estimate pressure losses in pipelines, ver-tical tubings, and other restrictions (chokes, safety valves) for mul-tiphase flow conditions. Also, methods and correlations are available to estimate reservoir deliverabifity from stabilized well test data with good accuracy. On the other hand, additional in-vestigation is required to improve methods for the prediction ol pressure losses across wefl completions (gravel pack and perfora-tions). Several of these methods and correlations used in this work are discussed in a later section. Calculation of over-all production performance of a system in-Keywords: Over-afl production performance, Production system, Produc-tion system components, Nodal analysis, Multiphase flow pressure loss correlations, Sensitivity analysis, Unstable flow condifions. volves a simple principle. This consists of dividing the production system into two parts at a fixed point (node), and for a given flow rate, starting for one part a...

Geostatistics techniques are being used increasingly to model reservoir heterogeneity at a wide range of scales. A variety of techniques is now available with differing underlying assumptions, complexity, and applications. This paper introduces a novel method of geostatistics to model dynamic gas-oil contacts and shales in the Prudhoe Bay reservoir.The method integrates reservoir description and surveillance data within the same geostatistical framework. Surveillance logs and shale data are transformed to indicator variables. These variables are used to evaluate vertical and horizontal spatial correlation and cross-correlation of gas and shale at different times and to develop variogram models. Conditional simulation techniques are used to generate multiple three-dimensional (3D) descriptions of gas and shales that provide a measure of uncertainty. These techniques capture the complex 3D distribution of gas-oil contacts through time.We compare results of the geostatistical method with conventional techniques as well as with infill wells drilled after the study. Predicted gas-oil contacts and shale distributions are in close agreement with gas-oil contacts observed at infill wells.

This psper wss prepsred for presenfstion at the International Meeting on Petroleum Engineering held inSaijing, PR China, 14-17 November 1995. Thw psper was selected for presentation by an SPE Pmgrsm Ccmmiftes following review of infonnafion contained in an abslrsct submitted by the aulhor(s). Contents of the paper, se presented, have no! &m reviewed bv the !%ociitv of Pa.troteum Engineers and are M?@cted to correction by ths sufhor(s). Ths msterisl, ss presanted, does 1101 tI~SSSNlrdfacf anyWstionOfthe ,------.,. society of Petroleum Engineers, its officers, or members. Pspers presented at SPE meetings sre subject to pubfiition review by Edtoti Commttees of the society of Petroleum Engineers. permisaicm to CCWYis restricted to an sbstrsct of not more than 200 WOKIS. Iffuafrationa may not be copied Ths sbstract should oontah conspicuous acknowledgment of where and by whom ti papar is presented Write Librsrisn, SPE, P.O. Sox S33S3S, Richardson, TX 750S2-3S36, U.S.A. (Facsimile 214-952-S435). AbstractMonitoring fluid movement is important for selecting infill locations and completion intervals and optimizing production operations in reservoirs producing under waterflood and gravity drainage mechanisms. of three-dimensional distributions of oil and gas in a gravity drainage area of Prudhoe Bay. The results of the methodology are in excellent agreement with actual data.