Nonlinear state-space modeling approaches to real-time autonomous geosteering

Miao, Yinsen; Kowal, Daniel R.; Panchal, Neilkunal; Vila, Jeremy; Vannucci, Marina

doi:10.1016/j.petrol.2020.107025

Cited by 6 publications

(7 citation statements)

References 17 publications

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“…Similar to the work in [13,19], in this work a particle filter is used to address the well-log interpretation problem. The states of interest are inclination θ t , azimuth ϕ t , TVD z t , true stratigraphic thickness (TST) of the formation T t , and RSD between the logging tool and the upper formation layer s t of the logging tool at the station t. The RSD is the distance between the wellbore and the formation boundary measured in the direction of the formation layer's TST as shown earlier in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, it involves monitoring the formation's dip angle and the fault throw (i.e., sudden vertical displacement) at each location along the wellbore. Another example (Figure 1, right) also employs the logging tool's location, but instead of considering the dip angle or fault throw, it utilizes the concept of Relative Stratigraphic Depth (RSD) to represent the location of the formation boundary [13].…”

Section: Bayesian Well-log Interpretationmentioning

confidence: 99%

“…In [14][15][16], the response of a resistivity tool to the formation rock is modeled as an electro-magnetic simulator and the ensemble Kalman filter (EnKF) is adopted to estimate the formation boundaries. More recent publications [13,[17][18][19] adopted sequential Monte Carlo techniques (i.e., particle filters) as a substitution of the Kalman filter. However, these aforementioned interpretation techniques only estimate the distance to or shape of nearby formation boundaries in the near-wellbore field.…”

Section: Bayesian Well-log Interpretationmentioning

confidence: 99%

“…Given a prior estimate of the formation boundaries (or some representation of the boundaries) along with new measurements taken along the wellbore, these methods infer the posterior distribution of the formation boundaries over the entire wellbore. However, due to the high dimensionality of this problem, the proposed model representations and solution methods are computationally intensive and do not always offer significant improvements over the most efficient recursive Bayesian filtering methods [13].…”

Section: Bayesian Well-log Interpretationmentioning

confidence: 99%

See 3 more Smart Citations

Real-Time Automated Geosteering Interpretation Combining Log Interpretation and 3D Horizon Tracking

D’Angelo,

Zhao,

Zhang

et al. 2024

Geosciences

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Existing methods for estimating formation boundaries from well-log data only analyze the formation along the wellbore, failing to capture changes in the 3D formation structure around it. This paper presents a method for real-time 3D formation boundary interpretation using readily available well logs and seismic image data. In the proposed workflow, the mean formation boundary is estimated as a curve following the well path. 3D surfaces are then fitted through this boundary curve, aligning with the slopes and features in the seismic image data. The proposed method is tested on both synthetic and field datasets and illustrates the capabilities of accurate boundary estimation near the well path and precise representation of boundary shape changes further away from the well trajectory. With this fully automated geological interpretation workflow, human bias and interpretation uncertainty can be minimized. Subsurface conditions can be continually updated while drilling to optimize drilling decisions and further automate the geosteering process.

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Section: Methodsmentioning

confidence: 99%

Section: Bayesian Well-log Interpretationmentioning

confidence: 99%

Section: Bayesian Well-log Interpretationmentioning

confidence: 99%

Section: Bayesian Well-log Interpretationmentioning

confidence: 99%

See 2 more Smart Citations

Real-Time Automated Geosteering Interpretation Combining Log Interpretation and 3D Horizon Tracking

D’Angelo,

Zhao,

Zhang

et al. 2024

Geosciences

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“…Instead of inferring formation parameters of fixed dimensionality, the authors in [57,58,59] used trans-dimensional MCMC methods to infer parameters of earth models that can have a varied number of layers. Miao et al [60] and Veettil et al [61] proposed to infer well-bore locations from gamma-ray (GR) logging data using Bayesian approaches. In this work, we solve the well-logging inverse problems using resistivity EM data with the GTMCMC algorithm, and compare its results with those obtained from the standard TMCMC method.…”

Section: Inversion Of Ultra-deep Directional Resistivity Measurementsmentioning

confidence: 99%

Generalized Transitional Markov Chain Monte Carlo Sampling Technique for Bayesian Inversion

Han¹,

Catanach²,

Chen³

et al. 2021

Preprint

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In the context of Bayesian inversion for scientific and engineering modeling, Markov chain Monte Carlo sampling strategies are the benchmark due to their flexibility and robustness in dealing with arbitrary posterior probability density functions (PDFs). However, these algorithms been shown to be inefficient when sampling from posterior distributions that are high-dimensional or exhibit multi-modality and/or strong parameter correlations. In such contexts, the sequential Monte Carlo technique of transitional Markov chain Monte Carlo (TMCMC) provides a more efficient alternative. Despite the recent applicability for Bayesian updating and model selection across a variety of disciplines, TMCMC may require a prohibitive number of tempering stages when the prior PDF is significantly different from the target posterior. Furthermore, the need to start with an initial set of samples from the prior distribution may present a challenge when dealing with implicit priors, e.g. based on feasible regions. Finally, TMCMC can not be used for inverse problems with improper prior PDFs that represent lack of prior knowledge on all or a subset of parameters. In this investigation, a generalization of TM-CMC that alleviates such challenges and limitations is proposed, resulting in a tempering sampling strategy of enhanced robustness and computational efficiency. Convergence analysis of the proposed sequential Monte Carlo algorithm is presented, proving that the distance between the intermediate distributions and the target posterior distribution monotonically decreases as the algorithm proceeds. The enhanced efficiency associated with the pro-

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Synchronous Optimization Schemes for Dynamic Systems Through the Kernel-Based Nonlinear Observer Canonical Form

Ding

2022

IEEE Trans. Instrum. Meas.

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Nonlinear state-space modeling approaches to real-time autonomous geosteering

Cited by 6 publications

References 17 publications

Real-Time Automated Geosteering Interpretation Combining Log Interpretation and 3D Horizon Tracking

Real-Time Automated Geosteering Interpretation Combining Log Interpretation and 3D Horizon Tracking

Generalized Transitional Markov Chain Monte Carlo Sampling Technique for Bayesian Inversion

Synchronous Optimization Schemes for Dynamic Systems Through the Kernel-Based Nonlinear Observer Canonical Form

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