Rapid Reservoir Modeling with Automated Tops Correlation

Grant, Christian W.; Bashore, William M.

doi:10.15530/urtec-2018-2904037

Cited by 5 publications

(3 citation statements)

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“…There is substantial interest in establishing methods that can provide automated and objective formation top correlations incorporating multiple wells (Hong and Kang, 2020) up to many hundreds of wells (Zoraster et al, 2004) or more directly while drilling (Al-AbdulJabbar et al, 2018). This automated requirement has become even more pertinent since the extensive development of unconventional gas and oil resources on a basin-wide scale involving thousands of wells tied to seismic data (Grant et al, 2018). As well as a requirement for field development studies, automated well-picking is also a potential benefit for basin-wide studies.…”

Section: Introductionmentioning

confidence: 99%

Well-log attributes assist in the determination of reservoir formation tops in wells with sparse well-log data

Wood

2023

Adv. Geo-Energy Res.

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Section: Introductionmentioning

confidence: 99%

Well-log attributes assist in the determination of reservoir formation tops in wells with sparse well-log data

Wood

2023

Adv. Geo-Energy Res.

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“…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%

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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“…Early attempts to use computers for log correlation have relied on the cross-correlation algorithm to find time-equivalent sample pairs (Rudman and Lankston, 1973;Mann and Dowell, 1978). 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 (Lineman et al, 1987;Fang et al, 1992;Zoraster et al, 2004;Hladil et al, 2010;Lallier et al, 2012;Wheeler and Hale, 2014;Wheeler, 2015;Lallier et al, 2016;Grant et al, 2018;Wu et al, 2018). Dynamic time warping has also been used to correlate paleo-proxy records of environmental change (e.g., Lisiecki and Lisiecki, 2002;Lisiecki and Raymo, 2005;Hay et al, 2019;Ajayi et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Automated stratigraphic correlation and model building using chronostratigraphic principles

Sylvester¹

2022

Preprint

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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-an-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. 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 dataset 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 is available.

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Rapid Reservoir Modeling with Automated Tops Correlation

Cited by 5 publications

References 0 publications

Well-log attributes assist in the determination of reservoir formation tops in wells with sparse well-log data

Well-log attributes assist in the determination of reservoir formation tops in wells with sparse well-log data

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

Automated stratigraphic correlation and model building using chronostratigraphic principles

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