Acid Fracturing Productivity Model for Naturally Fractured Carbonate Reservoirs
- Authors
- Assiya Ugursal (Texas A&M University) | Mateus Palharini Schwalbert (Texas A&M University) | Ding Zhu (Texas A&M University) | Alfred Daniel Hill (Texas A&M University)
- DOI
- https://doi.org/10.2118/191433-18IHFT-MS
- Document ID
- SPE-191433-18IHFT-MS
- Publisher
- Society of Petroleum Engineers
- Source
- SPE International Hydraulic Fracturing Technology Conference and Exhibition, 16-18 October, Muscat, Oman
- Publication Date
- 2018
- Document Type
- Conference Paper
- Language
- English
- ISBN
- 978-1-61399-622-5
- Copyright
- 2018. Society of Petroleum Engineers
- Disciplines
- 5.8 Unconventional and Complex Reservoirs, 4.1 Processing Systems and Design, 4 Facilities Design, Construction and Operation, 2 Well completion, 5 Reservoir Desciption & Dynamics, 3 Production and Well Operations, 4.1.2 Separation and Treating, 3 Production and Well Operations, 5.8.7 Carbonate Reservoir, 2.6 Acidizing
- Keywords
- leakoff, productivity, acid fracturing
- Downloads
- 12 in the last 30 days
- 155 since 2007
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In naturally fractured carbonates, the efficiency of acid fracturing stimulation can be hindered due to the decreased effective length and conductivity of created fractures, and acid loss into natural fractures is one of the main reasons for the reduced efficiency. At the same time, acid that leaks into natural fractures creates additional conductivity that may enhance production from the stimulated well. A model was developed to predict the acid fracturing performance in naturally fractured carbonate reservoir by taking into account the etching of both hydraulically induced and naturally occurring fractures to estimate fracture conductivity and well productivity.
The model uses a domain that contains a well and a rectangular reservoir. The well is intersected by a bi-wing vertical hydraulic fracture which is intersected by transverse natural fractures. The model simulates acid injection into the fracture system, acid-rock reaction, and width increase for both hydraulic and natural fractures. At the end of the acid injection, the conductivity of the fracture system is estimated, and the well stimulation efficiency is evaluated by calculating productivity increase and skin factor. This is done by simulating the production flow into the hydraulic and natural fractures using a coupled reservoir model without the need of an external reservoir simulator. In contrast to previously published acid fracturing models which calculate leakoff using the Carter's model, in this study, we developed a model that calculates the leakoff during acid injection by simulating the flow through porous media using a reservoir model, which includes both hydraulic and natural fractures.
In contrast to the Carter's leakoff model which assumes that fractures are spaced far enough so that no interaction among the fractures occurs, the new approach allows the natural fractures to interact with each other as acid leaks off and pressure changes in the reservoir surrounding the fractures. The new approach does not impose limitations on fracture spacing, and leakoff rate of individual natural fractures is a function of fracture spacing and location. The other feature of the new model is that the leakoff flow rate does not necessarily decrease with time, unlike what Carter's leakoff model predicts. It was observed that leakoff rate from natural fractures may increase initially as the natural fractures are stimulated. The effects of natural fracture geometry and spacing, reservoir permeability, and treatment conditions on acid leakoff, fracture conductivity and well productivity are analyzed. The role of natural fractures on stimulation efficiency is evaluated by comparing the results with the cases where no natural fractures are present in the reservoir.
The model enables a better prediction of acid fracturing performance in naturally fractured carbonate reservoirs, and also simulates more realistic leakoff behavior compared to the conventional leakoff model, which improves the accuracy of the results.
File Size | 1 MB | Number of Pages | 19 |
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