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Assessment of uncertainty of borehole resistivity measurements is important to quantify the accuracy of hydrocarbon reserves and production forecasts. We develop an efficient Bayesian inversion method for the quantitative interpretation of general borehole resistivity measurements. It enables the estimation of resistivity properties together with their uncertainties in conjunction with arbitrary sets of
Jian, Wang (Institute of Acoustics, Chinese Academy of Sciences) | Lei, Zhang (Institute of Acoustics, Chinese Academy of Sciences) | Hao, Chen (Institute of Acoustics, Chinese Academy of Sciences) | Xiu-ming, Wang (Institute of Acoustics, Chinese Academy of Sciences)
Real-time geosteering technology plays a key role in horizontal well development, which keeps the wellbore trajectories within target zones to maximize reservoir contact. Deep-directional-resistivity logging while drilling (LWD) tools have longer detection range and directionality to provide sufficient information for the operators, but meanwhile bring challenges to inversion of logging data, especially when the number of model layers is not fixed in priori. In this paper, we have developed an automatic inversion method to include the number of layers as a variable based on the trans-dimensional Markov chain Monte Carlo (MCMC) algorithm. The method assumes a 1D model based on planar layered formations penetrated by arbitrary well trajectories. In addition, a synthetic example demonstrates the inversion method can efficiently estimate the number of layers, positions, resistivities and also provide the probabilities of parameters without introducing bias.
Presentation Date: Wednesday, October 17, 2018
Start Time: 1:50:00 PM
Location: Poster Station 10
Presentation Type: Poster
We use a Bayesian inference approach, where knowledge of the parameters of interest is quantified in probability density functions.
Uncertainty in the transmitter position, theory error and insufficient model parameterization amongst various other factors can lead to significant correlated error in observed controlled source electromagnetic data. These errors come to light by an examination of the residuals after performing inversion. Since correlated error violates the assumption of independent data noise it can manifest in spurious structure in inverted models. We demonstrate this using both synthetic data and real data from Scarborough gas field, North West Australia. In this work we propose a method which uses a hierarchical Bayesian framework and reversible jump Markov chain Monte Carlo to account for correlated error. We find that this removes suspect structure from the inverted models and within reasonable prior bounds, provides information on the resolution of resistivity at depth.