Computer-assisted stochastic multi-well correlation: Sedimentary facies versus well distality

Baville, Paul; Apel, Marcus; Hoth, Silvan; Knaust, Dirk; Antoine, Christophe; Carpentier, Cédric; Caumon, Guillaume

doi:10.1016/j.marpetgeo.2021.105371

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…However, it is important to keep in mind that, even with high well densities, ChronoLog outputs are more laterally continuous and simpler than the real stratigraphy. Other approaches to computer‐based log correlation focus on generating stratigraphic gaps based on facies‐ and paleoenvironmental considerations (Baville et al, 2022; Edwards et al, 2018; Lallier et al, 2016); and the incorporation of such constraints is likely to improve the results.…”

Section: Discussionmentioning

confidence: 99%

“…biostratigraphy, seismic reflection surveys, lateral facies transitions, unconformities, onlaps) into the correlation process (e.g. Baville et al, 2022; Edwards et al, 2018; Julio et al, 2012; Wu & Caumon, 2017) have the potential to significantly improve a log‐based workflow like the one described here.…”

Section: Discussionmentioning

confidence: 99%

“…Early attempts to use computers for log correlation have relied on the cross‐correlation algorithm to find time‐equivalent sample pairs (Mann & Dowell, 1978; Rudman & Lankston, 1973). Later it became obvious that the dynamic time warping (DTW) algorithm handles better logs that differ significantly from each other, a common problem unless the stratigraphy is extremely simple (Baville et al, 2022; Fang et al, 1992; Grant et al, 2018; Hladil et al, 2010; Lallier et al, 2012, 2016; Lineman et al, 1987; Wheeler, 2015; Wheeler & Hale, 2014; Wu et al, 2018; Zoraster et al, 2004). Dynamic time warping has also been used to correlate paleo‐proxy records of environmental change (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Automated multi‐well stratigraphic correlation and model building using relative geologic time

Sylvester

2023

Basin Research

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Stratigraphic correlation of geophysical well logs is one of the most important—and most time‐consuming—tasks that applied geoscientists perform on a daily basis. Using the dynamic time warping (DTW) algorithm, automated correlation of two wells is a fairly simple task; DTW can also be used to correlate a large number of wells along a single path. However, errors accumulate along a path and loops cannot be closed. To create a three‐dimensionally consistent correlation framework, we use a Python implementation of the Wheeler and Hale (2014) approach, which is based on the idea of stretching and squeezing all logs into a chronostratigraphic diagram that has relative geologic time (RGT) on its y‐axis. The depth shifts needed for the RGT transformation are computed by translating the outputs of a large number of pairwise DTW correlations into a least‐squares optimization problem that is solved through the conjugate gradient method. The resulting chronostratigraphic diagram provides an overview of the overall stratigraphy and its variability; and the correlation framework is significantly different from what one could get from correlating based on lithologic similarity between two logs. To create geologically intuitive well‐log cross sections, we use a multi‐scale blocking method that relies on the continuous wavelet transform to identify stratigraphic units of a certain scale in one well and then propagate these boundaries to all the other wells. We demonstrate the usefulness of this approach on a data set with close to 700 wells from the Permian Basin, West Texas. Linear channel bodies in the deepwater Spraberry Formation are easily detected and clearly highlighted in maps and cross sections. The methodology is robust enough for mapping subtle stratigraphic details, previously considered feasible only through manual interpretation. More importantly, it can be used to quickly build three‐dimensional stratigraphic models for large segments of sedimentary basins where enough log data are available.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated multi‐well stratigraphic correlation and model building using relative geologic time

Sylvester

2023

Basin Research

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“…Dynamic time warping has been applied to geologic time series, with a focus on stratigraphic and paleoclimatic investigations (e.g. Haam and Huybers, 2010;Hay and others, 2019;Ajayi and others, 2020;Hagen and others, 2020;Baville and others, 2022). However, there have also been previous attempts to apply dynamic time warping to ice core records with varying success (e.g.…”

Section: Dynamic Time Warping Routine For Ice Core Synchronizationmentioning

confidence: 99%

Dynamic time warping to quantify age distortion in firn cores impacted by melt processes

Hagen,

Harper

2023

Ann. Glaciol.

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As warming intensifies across the Greenland ice sheet, an increasing number of shallow coring and radar studies are targeting the melt-impacted firn column to investigate meltwater processes. Highly inhomogeneous infiltration and refreezing, however, redistributes mass, distorting age–depth relationships and confounding comparisons between different cores. Here, we utilize a dynamic time warping algorithm for time series alignment to investigate and quantify the heterogeneous impact of melt processes on nine closely spaced (within 50 m) firn core-density profiles. The 10 m cores were collected relatively high in Greenland's percolation zone, where melt alteration is minimal compared to lower elevation. Our analysis demonstrates the effectiveness of dynamic time warping as a tool for assessing heterogeneity between ice core records. We find that the optimal alignment of density profiles in the nine cores requires vertical stretching and compression of individual profiles, ranging from, on average, <1 to ~16% of the core lengths. We identified four depth zones of mass redistribution that appear to coincide with observed ice layers. Further, ~75% of density measurements from each core do not align with an age model-derived density profile that assumes no mass redistribution of meltwater, indicating the pervasive impact of melt processes.

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Continuous Wavelet Transform and Dynamic Time Warping-Based Fine Division and Correlation of Glutenite Sedimentary Cycles

Wang²,

Wang³

2022

Math Geosci

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Computer-assisted stochastic multi-well correlation: Sedimentary facies versus well distality

Cited by 7 publications

References 37 publications

Automated multi‐well stratigraphic correlation and model building using relative geologic time

Automated multi‐well stratigraphic correlation and model building using relative geologic time

Dynamic time warping to quantify age distortion in firn cores impacted by melt processes

Continuous Wavelet Transform and Dynamic Time Warping-Based Fine Division and Correlation of Glutenite Sedimentary Cycles

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