Last updated on Apr 14, 2024

What are the best practices for integrating sedimentological data from outcrops, cores, and well logs?

Sedimentology is the study of the origin, composition, distribution, and evolution of sediments and sedimentary rocks. Sedimentological data can provide valuable insights into the depositional environment, paleoclimate, paleogeography, and diagenesis of sedimentary basins. However, sedimentological data are often heterogeneous, incomplete, and scale-dependent, requiring careful integration from different sources and methods. In this article, we will discuss some of the best practices for integrating sedimentological data from outcrops, cores, and well logs, and how they can improve your stratigraphic correlation and interpretation.

1 Outcrop data

Outcrop data are the most direct and comprehensive source of sedimentological information, as they expose the three-dimensional architecture, facies, and structures of sedimentary units. Outcrop data can be collected through field mapping, measuring sections, sampling, and photography, as well as remote sensing techniques such as lidar, drone imagery, and ground-penetrating radar. Outcrop data can help you to identify the depositional systems, sequence stratigraphy, and tectonic controls of the basin, as well as to calibrate and validate your subsurface models. However, outcrop data are also limited by accessibility, exposure quality, weathering, and lateral continuity, and may not represent the same stratigraphic interval or facies as your subsurface data.

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Amna Hussein

Geologist
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Integrating sedimentological data from outcrops, cores, and well logs is essential for understanding the depositional environment, facies distribution, and reservoir properties. By combining these different types of data, geoscientists and reservoir engineers can gain a more comprehensive understanding of subsurface geology and make informed decisions in hydrocarbon exploration and production. Here are some best practices for integrating sedimentological data from outcrops, cores, and well logs: 1. Establishing a Detailed Stratigraphic Framework 2. Facies Analysis and Classification 3. Petrophysical Analysis 4. High-Resolution Correlation 5. 3D Visualization and Modeling 6. Calibration with Modern Analogues

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Abdelkader Rezig

Graduate Oil Field Engineer | Geologist | Data Science Enthusiast | Seeking opportunities in Geology, Reservoir Engineering in the oil industry.
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Geologists usually learn the principles of sedimentology and stratigraphy from outcrops in the field. Outcrops can be vital in exploration studies as analogues and data points to reduce uncertainty in building models of the subsurface, at any scale. They provide detailed data to constrain the depositional and tectonic history of a basin, information on reservoir properties, and understanding subsurface architecture.

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Dr. Vinay K. Sahay

Director at MEPL Research Foundation
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To effectively integrate sedimentological data: #Standardize protocols for data collection. #Cross-validate information from multiple sources. #Consider geological context for accurate interpretation. #Ensure consistency in spatial and temporal scales. #Utilize advanced software for integration and visualization. #Foster interdisciplinary collaboration. #Continuously update and refine datasets.

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2 Core data

Core data are the most reliable and representative source of sedimentological information from the subsurface, as they preserve the original texture, composition, and structure of the sedimentary rocks. Core data can be collected through coring operations during drilling, or through core analysis in the laboratory, using techniques such as core description, petrography, geochemistry, and geomechanics. Core data can help you to characterize the lithology, porosity, permeability, mineralogy, and diagenesis of the reservoir, as well as to correlate and interpret your well logs. However, core data are also expensive, scarce, and biased, as they only sample a small fraction of the subsurface, and may be affected by drilling, handling, and storage artifacts.

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Abdelkader Rezig

Graduate Oil Field Engineer | Geologist | Data Science Enthusiast | Seeking opportunities in Geology, Reservoir Engineering in the oil industry.
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Sedimentary data from core are recorded in the same way as strata in outcrop by using graphic sedimentary logs. Cores are cut through â€˜ð’•ð’‚ð’“ð’ˆð’†ð’• ð’‰ð’ð’“ð’Šð’›ð’ð’ð’”â€™, that is, parts of the succession that have been identified from the interpretation of seismic interpretation as likely source rocks, or, more importantly, reservoir bodies. They help geologists to provide a sedimentological description and rock property analysis as well as correlation of well logs.

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3 Well log data

Well log data are the most abundant and continuous source of sedimentological information from the subsurface, as they record the physical properties of the sedimentary rocks along the wellbore. Well log data can be collected through logging tools during or after drilling, or through log analysis using software and algorithms. Well log data can help you to infer the lithology, facies, depositional environment, and stratigraphic framework of the basin, as well as to identify and quantify the reservoir quality and fluid content. However, well log data are also indirect, complex, and ambiguous, as they depend on the resolution, calibration, and interpretation of the logging tools, and may be influenced by borehole conditions, fluid invasion, and noise.

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Abdelkader Rezig

Graduate Oil Field Engineer | Geologist | Data Science Enthusiast | Seeking opportunities in Geology, Reservoir Engineering in the oil industry.
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ð–ðžð¥ð¥ ð¥ð¨ð ð ð¢ð§ð is necessary because geological sampling during drilling (cuttings) leaves a very imprecise record of the formations encountered. Entire formation samples can be brought to the surface by coring, but this is both slow and expensive. Logging is precise but it needs interpretation to bring a log to the level of geological or petrophysical experience, they will help calculate porosity, water saturation, moveable hydrocarbons, hydrocarbon density, and so on. Logging data is used to identify ð˜±ð˜³ð˜°ð˜¥ð˜¶ð˜¤ð˜µð˜ªð˜·ð˜¦ ð˜»ð˜°ð˜¯ð˜¦ð˜´, to determine the depth and thickness of zones, to distinguish between oil, gas, or water in a reservoir, and to estimate hydrocarbon reserves.

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4 Data integration

Data integration is the process of combining and synthesizing sedimentological data from different sources and methods, using a consistent and coherent approach. Data integration can improve your understanding of the sedimentary system, reduce your uncertainty and risk, and enhance your decision making and communication. Data integration can be performed at different levels, such as data visualization, data comparison, data fusion, and data modeling. Data integration can also involve different techniques, such as cross-plots, histograms, cluster analysis, principal component analysis, neural networks, and geostatistics.

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5 Integration challenges

Integration challenges are the difficulties and limitations that you may encounter when integrating sedimentological data from different sources and methods. Integration challenges can arise from various factors, such as data availability, data quality, data resolution, data compatibility, data heterogeneity, and data uncertainty. Integration challenges can affect your data integration performance, accuracy, and reliability, and may lead to errors, biases, and inconsistencies in your data integration results. Integration challenges can be overcome by applying appropriate data integration strategies, such as data selection, data standardization, data transformation, data validation, and data optimization.

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6 Integration best practices

Integration best practices are the recommendations and guidelines to achieve effective and efficient data integration. These practices can help to overcome integration challenges, enhance integration benefits, and achieve objectives. Depending on the context, purpose, and scope, integration best practices may vary; however, some general principles include defining goals and questions clearly and explicitly, reviewing and evaluating data sources and methods objectively, using multiple complementary data sources and methods to cross-check data, utilizing appropriate data integration levels and techniques to address goals, using consistent and coherent workflows and protocols to ensure quality and reproducibility, and documenting and communicating the process and results transparently.

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7 Hereâ€™s what else to consider

This is a space to share examples, stories, or insights that donâ€™t fit into any of the previous sections. What else would you like to add?

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Directory

What are the best practices for integrating sedimentological data from outcrops, cores, and well logs?

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1 Outcrop data

2 Core data

3 Well log data

4 Data integration

5 Integration challenges

6 Integration best practices

7 Hereâ€™s what else to consider

Sedimentology

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