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.
Acceptable data quality for formation evaluation forms the foundation for understanding the reservoir characterization, Petrophysical properties and pay zones identification. The data quality becomes more challenging in the thin-bed reservoirs (also known as ‘Low resistivity pay’ often abbreviated as LowReP), which poses a significant trial for field development to quantify the volumes in place and producibility. "Greater Dolphin Area" (GDA) located within the East Coast Marine Area (ECMA) off Trinidad consists of several fields out of which this paper will be focusing on the Dolphin and Starfish fields. The reservoirs consist of a series of stacked Pleistocene sands with good porosity and permeability within a three-way-dip closure against a large growth fault. Thin-beds have been observed, verified and documented throughout this area via core acquisition, core photographs and PLT analysis. As aforementioned, due to the low resistivity pay reservoirs characteristics in Starfish and Dolphin fields, these often been overlooked or interpreted to be water-bearing, when surveyed with conventional resistivity logging tools. These thinly bedded pay section hydrocarbon volumes have significantly been underestimated between 50% – 200% deposits. Several thin-bed methodologies have been employed for the Petrophysical modelling to support the field development planning using conventional legacy approach and Shell "Low Resistivity Pay" (LowReP) based tool response modelling and inversion methods workflow to resolve the issues associated with the presence of thin beds. This paper investigates the different methods of resolving the thin bed analysis problem and demonstrates the uncertainty of the results from each methodology on the Petrophysical properties and hydrocarbon volumes estimation.
Conventionally, a calibrated 1D geomechanical model is used to define the mud weight window required for the successful drilling and completion of a well. Utilizing depth stretch functionality, estimated rock properties and subsurface stresses, are ‘stretched’ from the corresponding offset well to the proposed well to be drilled. This approach will only suffice if the geological structure and wellbore trajectory are relatively simple. Even so, optimizing wellbore placement becomes an arduous exercise when using 1D geomechanical models because the workflow must be repeated for each new iteration of the proposed wellbore trajectory. Furthermore, as the geological structure and wellbore trajectory increases in complexity, severe distortion in topological properties, such as overburden stress and pore pressure, can render the one-dimensional solution inapplicable. In such circumstances, a calibrated 3D geomechanical model can be used. This paper introduces a generic workflow for developing a calibrated 3D geomechanical model that can be used for wellbore stability analysis. The workflow incorporates calibrated 1D geomechanical models and existing static geological modeling outputs, such as structural surfaces and facies model, to constrain the distribution of topological and primary properties within a 3D structural framework. The applicability of the workflow will be demonstrated by presenting the results of a case study from the Starfish field, ECMA, offshore Trinidad. It is intended for this paper to serve as a reference to geoscientists and engineers involved in brownfield and greenfield development planning. By extension, subsurface professionals who are involved in integrated reservoir modeling may also benefit from the work presented since geomechanics is often omitted from the modelling workflow.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.