Placement of a sufficient volume of acid in all desired zones is critical for a successful acid stimulation treatment. Particularly in thick, highly heterogeneous carbonate formations, the acid distribution is crucial for optimal stimulation results. A variety of diversion methods are applied in acidizing treatments to evenly place acid along the well, but the effectiveness of these diversion methods is generally only inferred from the rate and pressure behavior during the treatment, and is not known with any certainty. Recently, distributed temperature sensing technology has enabled us to observe dynamic temperature profiles along the wellbore during and immediately following an acid treatment. This technology allows us to monitor and evaluate treatments and diversion methods in real-time and to capture a sequence of temperature profiles at different times during and after acid injection.
We developed a transient thermal model for reservoir, coupled with a wormhole penetration model. Then the reservoir model is combined with a vertical well temperature model as the forward model, which can predict the temperature behavior inside formation and wellbore during and after a treatment. We applied the forward model in a synthetic two-layer example, and it shows that the temperature increase caused by the reaction between acid and carbonate rock indicates the acid distribution.
An inversion model was also developed to analyze the temperature data measured after treatments to obtain the acid flow profile for a vertical well. The inversion method applied in this work is Markov chain Monte Carlo (MCMC) method, which is a stochastic method to search globally for possible results. We discuss the approach to realize the inversion procedure and to make the inversion more efficient.
We also applied the comprehensive thermal model for hypothetical cases and field cases. The results from the inverse model give us quantitative understanding of acid distribution, which helps us to confirm the success of the acid treatment and diversion methods.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2012-08-11681 |
| Date | 2012 August 1900 |
| Creators | Tan, Xuehao |
| Contributors | Zhu, Ding, Hill, A. Daniel |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | thesis, text |
| Format | application/pdf |
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