A step toward the practical stratigraphic automatic correlation of well logs using continuous wavelet transform and dynamic time warping technique

Behdad, Ali

doi:10.1016/j.jappgeo.2019.05.007

Cited by 17 publications

(4 citation statements)

References 6 publications

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“…Schwarzacher, 1975;Hinnov and Park, 1998;Weedon, 2003;Martinez et al, 2016), and automatic stratigraphic correlations (e.g. Nio et al, 2005;Behdad, 2019;Shi et al, 2021) are examples of techniques used in stratigraphic research for quantifying cycles. With the socalled digital transformation currently in force in many areas of knowledge, such quantitative approaches tend to be expanded.…”

Section: Discussionmentioning

confidence: 99%

Cyclicity in Earth sciences, quo vadis? Essay on cycle concepts in geological thinking and their historical influence on stratigraphic practices

Fragoso

Kuchenbecker

Magalhães

et al. 2022

Hist. Geo Space. Sci.

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Abstract. The archetype of a cycle has played an essential role in explaining observations of nature over thousands of years. At present, this perception significantly influences the worldview of modern societies, including several areas of science. In the Earth sciences, the concept of cyclicity offers simple analytical solutions in the face of complex events and their respective products, in both time and space. Current stratigraphic research integrates several methods to identify repetitive patterns in the stratigraphic record and to interpret oscillatory geological processes. This essay proposes a historical review of the cyclic conceptions from the earliest phases in the Earth sciences to their subsequent evolution into current stratigraphic principles and practices, contributing to identifying opportunities in integrating methodologies and developing future research mainly associated with quantitative approaches.

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Section: Discussionmentioning

confidence: 99%

Cyclicity in Earth sciences, quo vadis? Essay on cycle concepts in geological thinking and their historical influence on stratigraphic practices

Fragoso

Kuchenbecker

Magalhães

et al. 2022

Hist. Geo Space. Sci.

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“…Well log stratification plays an integral role in geological data interpretation, significantly influencing lithology identification, log facies analysis, and reservoir parameter studies [1][2][3][4][5][6][7][8][9][10]. Conventional methodologies, which include both labor-intensive manual techniques and automated approaches, have inherent limitations.…”

Section: Introductionmentioning

confidence: 99%

DRAG: A Novel Method for Automatic Geological Boundary Recognition in Shale Strata Using Multi-Well Log Curves

Zhou,

Zhu,

Zhu

et al. 2023

Processes

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Ascertaining the positions of geological boundaries serves as a cornerstone in the characterization of shale reservoirs. Existing methods heavily rely on labor-intensive manual well-to-well correlation, while automated techniques often suffer from limited efficiency and consistency due to their reliance on single well log data. To overcome these limitations, an innovative approach, termed DRAG, is introduced, which uses deep belief forest (DBF), principal component analysis (PCA), and an enhanced generative adversarial network (GAN) for automatic layering recognition in logging curves. The approach employed in this study involves the use of PCA for dimensionality reduction across multiple well log datasets, coupled with a sophisticated GAN to generate representative samples. The DBF algorithm is then applied for stratification, incorporating a confidence screening mechanism to improve computational efficiency. In order to improve both accuracy and stability, a coordinate system is introduced that adjusts for stratification variations among neighboring wells around the target well. Experimental comparisons demonstrate the superior performance of the proposed algorithm in reducing stratification fluctuations and improving precision.

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“…Rudman and Lankston (1973), Mann and Dowell Jr (1978) identified a stratigraphic correlation, also called correlation, using the cross-correlation technique. Smith and Waterman (1980), Anderson and Gaby (1983), Howell(1983), Waterman and Raymond (1987), Fang et al (1992), Edwards et al (2018), Behdad (2019), and Le et al (2019) determined a similarity between two well-log sequences using the dynamic time warping, also called dynamic waveform matching technique. Zimmermann et al (2018), Brazell et al (2019), Bakdi et al (2020), Tokpanov et al (2020), andParimontonsakul (2021) focused on applying machine learning models in stratigraphic correlation identification.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Based Approach to Automate Stratigraphic Correlation through Marker Determination

2023

Improv Oil Gas Recover

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Stratigraphic correlation is well recognized as one of the essential processes, providing information regarding stratigraphic and compartmentalization in a reservoir. It becomes a starting point for subsurface evaluation processes ranging from reservoir characteristics to reserves and resources estimation and economic evaluation. It has always been a focus area in numerous traditional and modern research. Several practices approach stratigraphic correlation, including direct tracing from outcrop, relating geological markers, and comparing the organism characteristics. This work focuses only on one of the traditional work processes, utilizing geological markers to identify stratigraphic correlation. This work primarily studies the potential adoption of data analytics and machine learning in identifying geological markers and connecting them to derive stratigraphic correlation.Well logging information is the primary data source to interpret geological markers. Determining markers was previously done based on the specific well-log characteristics that are rare and uniquely identified in the geological area. It usually takes tremendous effort to find a particular marker from well logging information, especially when many wells scale up the works. Deriving computer-assisted technology with machine learning becomes a key enabler in accelerating and enhancing the business process. The machine learning assisted system has been trained with the entire geoscientists' marker interpretations.The system consists of two connected machine-learning models. The first model, designed as a multi-class classification, identifies the geological markers using well-logging information. The first model's predicted markers are then fed as an input to the second model, designed as a binary classification. It analyzes the relationship between markers in the same wellbore. Subsequently, the predicted markers resulting from two connected models are linked between two or more wells in the same region to create the stratigraphic correlation. Aiming to determine the practicality and potential adoption from one to another, this study implements the same model concept with two different sets of data, two fields in the Gulf of Thailand. The system has been proven successful in model development and deployment and has achieved nearly human performance levels.

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A step toward the practical stratigraphic automatic correlation of well logs using continuous wavelet transform and dynamic time warping technique

Cited by 17 publications

References 6 publications

Cyclicity in Earth sciences, quo vadis? Essay on cycle concepts in geological thinking and their historical influence on stratigraphic practices

Cyclicity in Earth sciences, quo vadis? Essay on cycle concepts in geological thinking and their historical influence on stratigraphic practices

DRAG: A Novel Method for Automatic Geological Boundary Recognition in Shale Strata Using Multi-Well Log Curves

A Machine Learning Based Approach to Automate Stratigraphic Correlation through Marker Determination

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