Statistical Bayesian Inversion of Azimuthal Resistivity Logging-while-drilling Data



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Learning about the inner earth is one critical objective of earth science. In a modern sense, a better understanding of the geological world supports the basic needs of earth resources. Industrial exploration and production perform drilling practice as one primary process that allows direct contact with the earth formation. Drilling a vertical well straightly down into the earth and extracting hydrocarbon from the reservoir is one frequent exercise in the conventional production. However, the introduction of more cutting-edge technologies makes many attracting applications possible. In the high angle and horizontal activities, the logging-while-drilling application provides controlled directional drilling and navigating through the guidance of real-time formation evaluation. It supports a maximized production from a single well. The vast amount of drilling measurements are obtained through multiple sensing systems and are sent back to the earth surface using a telemetry system. Interpreting the measurements and revealing the true structure of the geological world is never easy. The old experience and traditional mathematical tools seem insufficient to handle the complex, multi-types, and large quantity of the measured data. An effective data-driven solution is necessarily required to guarantee a robust interpretation of complex measurements. Under this background, the dissertation hereby illustrates a class of statistical methods stressing on the geological inverse problems which interpret logging measurements and reconstruct the formation model properties. The methods governed by Bayesian inference are used to reveal the probability of an earth model given the measurements. The solution is returned via a collection of model samples instead of a unique value. It provides a way to assess model uncertainty through the statistics. Several Markov chain Monte Carlo methods are introduced and studied through their implementation of the real-world inverse problems. The methods are generalized to be a data-driven approach that can handle the more advanced tasks that require a reservoir-scale evaluation. It can also deal with the interpretation of model complexity, which lessens the human intervention and guarantees a robust data-driven analyzing scheme.



Bayesian inference, Statistical Inversion, Logging-while-drilling, Azimuthal resistivity tool


Portions of this document appear in: Shen, Qiuyang, Xuqing Wu, Jiefu Chen, and Zhu Han. "Distributed Markov Chain Monte Carlo Method on Big-Data Platform for Large-Scale Geosteering Inversion Using Directional Electromagnetic Well Logging Measurements." Applied Computational Electromagnetics Society Journal 32, no. 5 (2017). And in: Shen, Qiuyang, Xuqing Wu, Jiefu Chen, Zhu Han, and Yueqin Huang. "Solving geosteering inverse problems by stochastic Hybrid Monte Carlo method." Journal of Petroleum Science and Engineering 161 (2018): 9-16. And in: Shen, Qiuyang, Jiefu Chen, and Hanming Wang. "Data-Driver Interpretation of Ultradeep Azimuthal Propagation." Petrophysics 59, no. 6 (2018). And in: Lu, Han, Qiuyang Shen, Jiefu Chen, Xuqing Wu, and Xin Fu. "Parallel multiple-chain DRAM MCMC for large-scale geosteering inversion and uncertainty quantification." Journal of Petroleum Science and Engineering 174 (2019): 189-200.