Rapid Reservoir Modeling: Prototyping With an Intuitive, Sketch-Based Interface
Authors: Wilson Adam 
Published: June 2016

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 173237, “Rapid Reservoir Modeling: Prototyping of Reservoir Models, Well Trajectories, and Development Options With an Intuitive, Sketch-Based Interface,” by M.D. Jackson, SPE, G.J. Hampson, and D. Rood, Imperial College London; S. Geiger, SPE, and Z. Zhang, Heriot-Watt University; M.C. Sousa, SPE, R. Amorim, E. Vital Brazil, and F.F. Samavati, University of Calgary; and L.N. Guimaraes, SPE, University of Pernambuco, prepared for the 2015 SPE Reservoir Simulation Symposium, Houston, 23–25 February. The paper has not been peer reviewed.

Constructing and refining complex reservoir models are challenging and time-consuming tasks that entail a high degree of uncertainty. Conventional modeling work flows have remained essentially unchanged for the past decade. Such work flows are poorly suited to rapid prototyping of a range of reservoirmodel concepts, well trajectories, and development options and to testing of how these might affect reservoir behavior. A new reservoir-modeling and -simulation approach, termed rapid reservoir modeling (RRM), allows such prototyping and complements existing work flows.

Introduction

Hydrocarbon reservoirs typically contain an array of complex geologic heterogeneities that are at or below the resolution of seismic data, so their geometry and spatial distribution are uncertain. These heterogeneities may be structural, stratigraphic, sedimentologic, or diagenetic in origin and often affect flow behavior and hydrocarbon recovery; hence, they must be captured in reservoir models. Reservoir-modeling work flows have remained essentially unchanged for the past decade, facilitated by commercially available software packages. These work flows begin with the construction of a geocellular reservoir model, in which a largely deterministic structural and stratigraphic framework is used to define the overall reservoir volume, and compartments and zones within the reservoir. A grid is then constructed within each zone, typically using pillars that are continuous from the top to the base of the modeled volume, and using layers that may vary in thickness or be truncated by reservoir-zone boundaries. Geostatistical methods are used to populate each grid cell with a geologic indicator (such as facies or rock type) and associated petrophysical properties. The resulting models typically contain several millions to tens of millions of cells and may be upscaled onto a coarser grid before flow simulation. Despite its wide use, there are a number of shortcomings with this work flow, including

Conventional modeling workflows are slow, often requiring many months from the development of initial model concepts to flow simulation or other outputs.

Conceptual geologic models become fixed early in the modeling process, with uncertainty explored using geostatistical methods within the framework of a single conceptual model, rather than across a range of possible geologic concepts.

It is difficult or impossible to explore rapidly a range of conceptual models, well trajectories, and development options and test how these might affect reservoir behavior.

The introduction of pillar grids early in the modeling workflow limits the spatial resolution of the model and the complexity of the geologic architectures that can be captured and focuses modeling efforts on population of gridblocks with rock properties by use of geostatistical modeling methods.

Geostatistical methods are often nonintuitive and require inputs that are not closely linked to the underlying geologic concept, so it can be difficult for the geologist to create a digital version of the model concept.

Integration across different disciplines is made more difficult by the use of different software tools and by different model grid types and resolutions.

The aim of this work is to develop RRM software for prototyping of complex reservoir models, well trajectories, and development options by means of novel, sketch-based interaction and modeling coupled with exploratory visualization and close-to-real-time numerical analysis. The new approach does not replace existing work flows; rather, it supplements them by allowing rapid testing of geologic and development concepts and how these affect reservoir behavior.