Evaluating Multiple Methods to Determine Porosity from Drilling Data

Cedola, A. E.; Atashnezhad, A.; Hareland, Geir

doi:10.2118/185115-ms

Cited by 2 publications

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Artificial Intelligent Logs for Formation Evaluation Using Case Studies in Gulf of Mexico and Trinidad & Tobago

Gan

Kumar

Ehiwario

et al. 2019

Day 3 Wed, October 02, 2019

View full text Add to dashboard Cite

Borehole-log data acquisition accounts for a significant proportion of exploration, appraisal and field development costs. As part of Shell technical competitive scoping, there is an ambition to increase formation evaluation value of information by leveraging drilling and mudlogging data, which traditionally often used in petrophysical or reservoir modelling workflow. Often data acquisition and formation evaluation for the shallow hole sections (or overburden) are incomplete. Logging-while-drilling (LWD) and/or wireline log data coverage is restricted to mostly GR, RES and mud log information and the quality of the logs varied depending on the vendor companies or year of the acquisition. In addition, reservoir characterization logs typically covered only the final few thousand feet of the wellbore thus preventing a full quantitative petrophysical, geomechanical, geological correlation and geophysical modelling, which caused limited understanding of overburden sections in the drilled locations and geohazards risls assessment. Use of neural networks (NN) to predict logs is a well-known in Petrophysic discipline and has often used technology since more than last 10 years. However, the NN model seldon utilized the drilling and mudlogging data (due to lack of calibration and inconsistency) and up until now the industry usually used to predict a synthetic log or fill gaps in a log. With the collaboration between Shell and Quantico, the project team develops a plug-in based on a novel artificial intelligence (AI) logs workflow using neural-network to generate synthetic/AI logs from offset wells logs data, drilling and mudlogging data. The AI logs workflow is trialled in Shell Trinidad & Tobago and Gulf of Mexicooffshore fields. The results of this study indicate the neural network model provides data comparable to that from conventional logging tools over the study area. When comparing the resulting synthetic logs with measured logs, the range of variance is within the expected variance of repeat runs of a conventional logging tool. Cross plots of synthetic versus measured logs indicate a high density of points centralized about the one-to-one line, indicating a robust model with no systematic biases. The QLog approach provides several potential benefits. These include a common framework for producing DTC, DTS, NEU and RHOB logs in one pass from a standard set of drilling, LWD and survey parameters. Since this framework ties together drilling, formation evaluation and geophysical data, the artificial intelligence enhances and possibly enables other petrophysical/QI/rock property analysis that including seismic inversion, high resolution logs, log QC/editing, real-time LWD, drilling optimization and others.

show abstract

Artificial Intelligent Logs for Formation Evaluation Using Case Studies in Gulf of Mexico and Trinidad & Tobago

Gan

Kumar

Ehiwario

et al. 2019

Day 3 Wed, October 02, 2019

View full text Add to dashboard Cite

show abstract

Evaluation of Reservoir Quality and Forecasted Production Variability along a Multi-Fractured Horizontal Well. Part 1: Reservoir Characterization

et al. 2021

View full text Add to dashboard Cite

Completion design for horizontal wells is typically performed using a geometric approach where the fracturing stages are evenly distributed along the lateral length of the well. However, this approach ignores the intrinsic vertical and horizontal heterogeneity of unconventional reservoirs, resulting in uneven production from hydraulic fracturing stages. An alternative approach is to selectively complete intervals with similar and superior reservoir quality (RQ) and completion quality (CQ), potentially leading to improved development efficiency. In the current study, along-well reservoir characterization is performed using data from a horizontal well completed in the Montney Formation in western Canada. Log-derived petrophysical and geomechanical properties, and laboratory analyses performed on drill cuttings, are integrated for the purpose of evaluating RQ and CQ variability along the well. For RQ, cutoffs were applied to the porosity (>4%), permeability (>0.0018 mD), and water saturation (<20%), whereas, for CQ, cutoffs were applied to rock strength (<160 Mpa), Young’s Modulus (60–65 GPa), and Poisson’s ratio (<0.26). Based on the observed heterogeneity in reservoir properties, the lateral length of the well can be subdivided into nine segments. Superior RQ and CQ intervals were found to be associated with predominantly (massive) porous siltstone facies; these intervals are regarded as the primary targets for stimulation. In contrast, relatively inferior RQ and CQ intervals were found to be associated with either dolomite-cemented facies or laminated siltstones. The methods developed and used in this study could be beneficial to Montney operators who aim to better predict and target sweet spots along horizontal wells; the approach could also be used in other unconventional plays.

show abstract

Evaluating Multiple Methods to Determine Porosity from Drilling Data

Cited by 2 publications

References 18 publications

Artificial Intelligent Logs for Formation Evaluation Using Case Studies in Gulf of Mexico and Trinidad & Tobago

Artificial Intelligent Logs for Formation Evaluation Using Case Studies in Gulf of Mexico and Trinidad & Tobago

Evaluation of Reservoir Quality and Forecasted Production Variability along a Multi-Fractured Horizontal Well. Part 1: Reservoir Characterization

Contact Info

Product

Resources

About