Abstract

A recently developed code to model hydrocarbon migration and convective time of flight makes use of complex analysis methods (CAM) paired with Eulerian particle tracking. Because the method uses new algorithms that are uniquely developed by our research group, validation of the fast CAM solutions with independent methods is merited. Particle path solutions were compared with independent solutions methods (Eclipse). These prior and new benchmarks are briefly summarized here to further verify the results obtained with CAM codes. Pressure field solutions based on CAM are compared with independent embedded discrete fracture method (EDFM) solutions. The CAM method is particularly attractive because its grid-less nature offers fast computation times and unlimited resolution. The method is particularly well suited for solving a variety of practical field development problems. Examples are given for fast optimization of waterflood patterns. Another successful application area is the modeling of fluid withdrawal patterns in hydraulically fractured wells. Because no gridding is required, the CAM model can compute the evolution of the drained rock volume (DRV) for an unlimited (but finite) number of both hydraulic and natural fractures. Such computations of the DRV are based on the convective time of flight and show the fluid withdrawal zone in the reservoir. In contrast, pressure depletion models are based on the diffusive time of flight. In ultra-low permeability reservoirs, the pressure depletion zones do not correspond to the DRV, because the convective and diffusive displacement rates differ over an order of magnitude (diffusive time of flight being the fastest). Therefore, pressure depletion models vastly overestimate the drained volume in shale reservoirs, which is why fracture and well spacing decisions should be based on both pressure depletion and DRV models, not pressure only.

Highlights

  • The petroleum industry is continually searching for faster tools to model and optimize field development methods

  • In ultra-low permeability reservoirs, the pressure depletion zones do not correspond to the drained rock volume (DRV), because the convective and diffusive displacement rates differ over an order of magnitude

  • We developed a streamline simulator based on complex analysis methods (CAM) that is grid-less and can give instantaneous streamline solutions for flow tubes resulting from certain constant and/or variable well rates

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Summary

Introduction

The petroleum industry is continually searching for faster tools to model and optimize field development methods. We developed a streamline simulator based on complex analysis methods (CAM) that is grid-less and can give instantaneous streamline solutions for flow tubes resulting from certain constant and/or variable well rates. Because of the strides made with the grid-less CAM code based on algorithms that are relatively new and developed by only our one research group, verification of the CAM results will help overcome the skepticism of practitioners and critics who are still unfamiliar with this modeling tool. This paper briefly reviews some earlier key CAM results with particular emphasis on their verification with independent, currently accepted methods for modeling flow in hydrocarbon reservoirs. The mathematical details (Section 2) of the method are only briefly stated in this concise review paper, to enable a discourse that focuses on the visual matches of the results, limiting assumptions, and future potential of the CAM approach. For an in-depth discussion of the key algorithms used, the reader is encouraged to consult our previous studies [1,2,4,15]

Brief Overview of CAM Methodology and Algorithms
Natural
Flow Paths and Convective Time of Flight
Application in Flood Studies
Case A—Sweep Patterns and Water Cut Ratios
CAM individual
Application to Fractured
Case A—Permeable Fractures
Case B—Impervious Fractures
Streamlines
Benchmarking
10. Pressure
Application to Hydraulically Fractured Wells
Discussion
Limitations
Model Strengths and Limitations
Future Model Strengths and Limitations
Conclusions

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