Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
An algorithm for non-stationary spatial modelling using multiple secondary variables is developed herein, which combines geostatistics with quantile random forests to provide a new interpolation and stochastic simulation. This paper introduces the method and shows that its results are consistent and similar in nature to those applying to geostatistical modelling and to quantile random forests. The method allows for embedding of simpler interpolation techniques, such as kriging, to further condition the model. The algorithm works by estimating a conditional distribution for the target variable at each target location. The family of such distributions is called the envelope of the target variable. From this, it is possible to obtain spatial estimates, quantiles and uncertainty. An algorithm is also developed to produce conditional simulations from the envelope. As they sample from the envelope, realizations are therefore locally influenced by relative changes of importance of secondary variables, trends and variability.
An algorithm for non-stationary spatial modelling using multiple secondary variables is developed herein, which combines geostatistics with quantile random forests to provide a new interpolation and stochastic simulation. This paper introduces the method and shows that its results are consistent and similar in nature to those applying to geostatistical modelling and to quantile random forests. The method allows for embedding of simpler interpolation techniques, such as kriging, to further condition the model. The algorithm works by estimating a conditional distribution for the target variable at each target location. The family of such distributions is called the envelope of the target variable. From this, it is possible to obtain spatial estimates, quantiles and uncertainty. An algorithm is also developed to produce conditional simulations from the envelope. As they sample from the envelope, realizations are therefore locally influenced by relative changes of importance of secondary variables, trends and variability.
Historically it has been a challenge to rapidly produce a geomodel that can honor the detailed form of complex faulting and folding, while enabling sensible property modeling and that is tailored to fluid flow simulations. In structurally complex areas, the construction of accurate 3D geological models is often impeded by the complexity of the fault framework, the resulting layer segmentation, "multi-z" horizons in compressive settings and steeply dipping to overturned layers. In particular, standard geocellular models, such as pillar grids, may fail to honor complex structural features. To address those issues, methodologies using a mapping between the geological space and a 3D parametric space — often referred to as depositional space — have been described in the literature for geological grid construction and property population. Using case examples of structurally complex settings, we illustrate a depositional unstructured grid construction workflow. Compared to known methodologies, the depositional space is computed using a geomechanically-based approach. We illustrate that the methodology allows for complex structural configurations to be effectively modeled and transformed into a geocellular model honoring the full structural complexity. Our depositional unstructured model can then be populated with properties and used directly for flow simulations.
As the oil and gas industry is moving toward tapping reserves in more complex structural environments, there comes the challenge for the reservoir modeling platform to accurately and robustly build and simulate a model to aid in making more trustworthy reserve estimates and field development plans. The 3D geocellular model sits at the core of an integrated seismic-to-simulation workflow, within which one can characterize and predict the behavior of reservoirs and can make confident quantitative decisions about one's assets. In structurally complex areas, the construction of accurate 3D models is often impeded by fundamental limitations of standard geocellular modeling technologies. With these limitations in mind, a new cut-cell unstructured grid has been developed that honors the geological structure precisely, enables accurate property modeling in a flattened, un-faulted, pseudo-depositional space, and can be simulated directly in a next-generation simulator; we call this unstructured grid built in depositional space a ‘depogrid’. The polyhedral, cut-cell nature of the depogrid arises when the regularly gridded volume in depositional space is cut by the structural model features (faults and unconformities) before being forward-deformed into geological space. By construction, the grid cell columns are orthogonal to the local stratigraphy, yet they can accurately represent complex structures and volumes, independent of the grid resolution. A next-generation high resolution reservoir seamlessly consumes the globally unstructured grid topology, with the structural complexity and non-neighbor connections, and honors the flow dynamics accurately. This ensures a more geologically consistent simulation model, realistic parameters to tune for history matching workflows and the ability to make reliable predictions. We present some particular reservoir modeling and simulation considerations where the depogrid approach improves on typical gridding technologies. The seismic-to-simulation workflow is then applied to several structural examples to demonstrate how the depogrid is best suited to model the geological structure and properties in a variety of reservoirs and subsequently improves the accuracy and efficiency of field development planning and risk mitigation.
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.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.