Integrated Modelling Workflow for Life Cycle Development of a Large Scale Coal Seam Gas Field

Abstract

Development of coal seam gas fields is conceptually simple but complexity arises with: the stochastic nature of coal reservoirscontinually changing work scopethe large number of wells required to meet gas contracts. In the current environment, the cost of developing thousands of wells and hundreds of kilometres of associated gathering is a key driver to the success or failure of CSG projects. Continuous reduction in cost/funding with limited resources drives companies to derive an integrated approach to the field development. Subsurface models now form an integral part of production forecasting and decision making. Companies have benefited from the computation technological advances in the recent past, whereby it is possible to run large-scale models in a reasonable timeframe. Several tools and approaches are available today to integrate complex 3D reservoir models with surface networks to generate an integrated production forecast. In this paper we focus on using advanced geospatial applications with integrated system models to derive a development concept which is optimal, realistic and capable of adapting to changes in work scope as the development progresses. Gathering routes and associated material take off (MTO) points are generated in geographic information system (GIS) tools, using constraints and criteria such as: access and approvalssub-surface data (scope of recovery maps, net coal and permeability)maximum use of existing infrastructure (Roads, Tracks, etc.)environmental constraints (overland flow, vegetation, etc.)well spacing. Seamless integration of GIS tools and sub-surface modeling tools is what makes this workflow unique. GIS tools acts as a key integrator, forcing different disciplines and departments to work together in a common platform. It also functions as a common database used across an entire organisation. GIS toolbox gives a significant head-start to the project by first defining what is achievable. It is then finessed with the best value sub-surface outcome and a final forecast is derived in a significantly shorter time scale. With the approach presented in this paper, the forecasting cycle, involving full economic run, is substantially reduced– from several months to just weeks, if not days. The final outcome is an achievable well sequence which is derived along a realistic gathering route. With this, the MTO and the production forecasts are aligned and the associated costs can be easily traced to source. This workflow is automated and can be easily repeated if scope or project premise changes. Last but not least, this approach can be applied to any onshore unconventional or conventional plays.

Stochastic Modeling for Estimating Coalbed Methane Resources

“…Based on these probabilistic resources, 3D static models with resources closest to the P90, P50, and P10 values were selected and set as the low, middle, and high probabilistic resource models, respectively, to be used for future reservoir simulation and well planning. 32 As can be seen from Figure 12, for these three models, the resource distributions are different but with the same trend. In other words, the resources are always high in the central and western part of the study area.…”

“…As shown in Figure , the distributions of the frequency and cumulative probability were plotted, from which the P90, P50, and P10 were determined. Based on these probabilistic resources, 3D static models with resources closest to the P90, P50, and P10 values were selected and set as the low, middle, and high probabilistic resource models, respectively, to be used for future reservoir simulation and well planning . As can be seen from Figure , for these three models, the resource distributions are different but with the same trend.…”

An On-Demand Scalable Model for Geographic Information System (GIS) Data Processing in a Cloud GIS

“…Many scholars have carried out useful explorations on the problem of business process expansion in GIS systems. Some researchers have studied how to use workflow technology to combine algorithm functions in existing GIS software to solve complex problems in environmental research [7][8][9]. Some have studied how to combine geographic information services on the internet to obtain more powerful geographic information services [10][11][12].…”