Maximize the placement of wells and production in unconventional reservoirs:Part 1
- Keith Richard Holdaway (SAS Institute Inc.)
- Document ID
- Society of Petroleum Engineers
- SPE Middle East Unconventional Gas Conference and Exhibition, 23-25 January, Abu Dhabi, UAE
- Publication Date
- Document Type
- Conference Paper
- 2012. Society of Petroleum Engineers
- 2.2.2 Perforating, 7.6.4 Data Mining, 1.5 Drill Bits, 5.6.9 Production Forecasting, 7.6.6 Artificial Intelligence, 5.5 Reservoir Simulation, 1.10.1 Drill string components and drilling tools (tubulars, jars, subs, stabilisers, reamers, etc), 1.6.1 Drilling Operation Management, 5.1 Reservoir Characterisation, 5.1.1 Exploration, Development, Structural Geology, 2 Well Completion, 1.6 Drilling Operations, 1.12.6 Drilling Data Management and Standards, 2.5.2 Fracturing Materials (Fluids, Proppant), 3.3.1 Production Logging, 3.2.3 Hydraulic Fracturing Design, Implementation and Optimisation, 1.6.6 Directional Drilling, 5.8.2 Shale Gas, 1.6.2 Technical Limit Drilling, 1.2.2 Geomechanics, 4.6 Natural Gas, 5.1.5 Geologic Modeling, 1.2.3 Rock properties, 5.8.4 Shale Oil
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Over the past decade significant supplies of natural gas have been discovered in shale. While the development of new technologies has driven down the cost of gas extraction, pursuing natural gas in shale continues to be risky and capital intensive. Producers seek the most productive zones in their shale basins, as well as continued improvement in hydraulic fracturing processes. Decreasing costs and reducing risk while maximizing shale gas production necessitates innovative, advanced analytical capabilities that can give you a comprehensive understanding of the reservoir heterogeneity in order to extract hidden predictive information, identify drivers and leading indicators of efficient well production, determine the best intervals for stimulation, and recommend optimum stimulation processes and frequencies. Modeling, simulating and predicting well productivity requires integrated exploratory, predictive and forecasting capabilities underpinned by advanced analytical models to unlock the true potential of each wellbore. Without the critical insight enabled by integrated analysis to pair productivity analysis with economic feasibility, companies face significant risk and uncertainty when developing new wells or optimizing production of extant wellbores.
This paper walks through two case studies implemented in the Bakken and Pinedale assets in the United States, exemplifying data mining workflows that successfully improved hydrocarbon production.
It is critical to assess via data mining methodologies the variability in and potential of well performance in order to formulate an optimized suite of well completion and reservoir development strategies. Owing to the inherent complexity of subsurface systems, a data driven set of advanced analytical workflows that embrace exploratory data analysis in a multivariate perspective and predictive data analysis must be implemented in order to complement the first principals that underpin the array of geoscientific schools of thought.
Bakken Formation - Case Study
The Bakken formation is a rock unit of the Late Devonian to Early Mississippian age that stretches beneath areas of Montana and North Dakota in the USA and Manitoba and Saskatchewan in Canada; see Figure 1. The most recent estimates of technically recoverable and non-recoverable with extant technology top out at 18 billion barrels of oil equivalent (BOE).
Porosities in the Bakken average about 5%, and permeabilities are very low, averaging 0.04 millidarcies, but the presence of vertical to sub-vertical natural fractures makes the Bakken an excellent candidate for horizontal drilling techniques and hydraulic fracturing.
|File Size||2 MB||Number of Pages||10|