Construction of Structurally and Stratigraphically Consistent Structural Models Using the Volume-Based Modelling Technology: Applications to an Australian Dataset

Abstract

This paper describes new techniques and algorithms for building consistent and accurate structural models of the subsurface in structurally and stratigraphically complex areas, and proposes an innovative workflow for iteratively building large-scale models in presence of sparse or noisy data. Contrary to traditional 3D modeling approaches, which consist in modeling first geological interfaces independently from each other before gluing them together to form a watertight representation of geological layers, the proposed technique creates first a 3D unstructured mesh representing the volume of interest, which is then sliced and diced by geological horizons and faults. It is based on a global interpolation method in which a scalar property representing the relative stratigraphic age of the formations is interpolated everywhere in the volume of interest. In the process, the geometry of each horizon is effectively constrained by interpretation data of all other horizons belonging to the same conformable sequence. The interpolation is practically insensitive to the complexity of the fault network (X faults, Y faults, thrusts) and yields a high resolution, 3D representation of geological layers, allowing the extraction of main horizons based on seismic input and of closely spaced intermediate horizon surfaces defined by well data. The proposed technique ensures the preservation of four key quality features:Geometrical accuracy: surfaces are as smooth as possible while fitting all input data geometry.Topological correctness: all model contacts are perfect and watertight.Stratigraphic consistency: horizon surfaces cannot cross each other; layering and truncation patterns dictated by the input stratigraphic column are honored everywhere in the model.Structural coherency: variations of fault displacement should be consistent with their size; layer thickness changes should be as regular as possible. These benefits are illustrated through the step-by-step construction and refinement of a large scale geological model located offshore Western Australia. It is also demonstrated that this technique and methodology can successfully be applied to a large variety of structural and stratigraphic contexts. Introduction The standard way of interpreting and modeling subsurface reservoirs establishes a clear separation between the creation of a "container" conforming to the reservoir structure and the interpolation of geological or petrophysical properties. The property population phase commonly leverages comprehensive information from well logs and from the seismic signal, through the use of pre- or post-stack attributes, of quantitative interpretation techniques and of seismic inversion. Conversely, the creation of a structural model traditionally only exploits very limited data interpreted along the main structural or stratigraphic interfaces. There is, however, a much larger amount of structural and stratigraphic information that can be extracted from seismic or log data. Moreover, while traditional workflows are mostly one-way, from the interpretation to the model creation, stratigraphically and structurally consistent models could and should be used to guide the refinement of seismic or well interpretation. This article introduces the building blocks of technology and workflows enabling (1) the incorporation of a vast amount of seismic constraints in structural models and (2) the use of interpolated structural information to iteratively enrich interpretations.

DeepISMNet: three-dimensional implicit structural modeling with convolutional neural network

“…The discrete smooth interpolation (DSI) is one class of implicit methods that computes structural models by discretizing the scalar function on a volumetric mesh (Mallet, 1988(Mallet, , 1992(Mallet, , 1997(Mallet, , 2014Souche et al, 2014;Renaudeau et al, 2019). In DSI and its variant approaches, structural modeling is performed by solving a least squares minimization problem with smooth constraints to compute a scalar field compatible with the inputs.…”

DeepISMNet: Three-Dimensional Implicit Structural Modeling with Convolutional Neural Network

“…The discrete smooth interpolation (DSI) is one class of implicit methods that computes structural models by discretizing the scalar function on a volumetric mesh (Mallet, 1988(Mallet, , 1992(Mallet, , 1997(Mallet, , 2014Souche et al, 2014;Renaudeau et al, 2019). In DSI and its variant approaches, structural modeling is performed by solving a least squares minimization problem with smooth constraints to compute a scalar field compatible with the inputs.…”