Practical Aspects of Reserve Determinations for Shale Gas

Strickland, Richard F.; Purvis, Dwayne Christopher; Blasingame, Thomas Alwin

doi:10.2118/144357-ms

Cited by 22 publications

(7 citation statements)

References 19 publications

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“…Despite the common practice in shale modeling using a conventional approach, which is usually done at the well level (Strickland et al, 2011), this technique is capable of performing history matching for all individual wells in addition to full field by taking into account the effect of offset wells.…”

Section: Top-down Modeling E Pattern Recognition Based Reservoir Modementioning

confidence: 99%

Full field reservoir modeling of shale assets using advanced data-driven analytics

Esmaili

Mohaghegh

2016

Geoscience Frontiers

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a b s t r a c tHydrocarbon production from shale has attracted much attention in the recent years. When applied to this prolific and hydrocarbon rich resource plays, our understanding of the complexities of the flow mechanism (sorption process and flow behavior in complex fracture systems -induced or natural) leaves much to be desired. In this paper, we present and discuss a novel approach to modeling, history matching of hydrocarbon production from a Marcellus shale asset in southwestern Pennsylvania using advanced data mining, pattern recognition and machine learning technologies. In this new approach instead of imposing our understanding of the flow mechanism, the impact of multi-stage hydraulic fractures, and the production process on the reservoir model, we allow the production history, well log, completion and hydraulic fracturing data to guide our model and determine its behavior. The uniqueness of this technology is that it incorporates the so-called "hard data" directly into the reservoir model, so that the model can be used to optimize the hydraulic fracture process. The "hard data" refers to field measurements during the hydraulic fracturing process such as fluid and proppant type and amount, injection pressure and rate as well as proppant concentration. This novel approach contrasts with the current industry focus on the use of "soft data" (non-measured, interpretive data such as frac length, width, height and conductivity) in the reservoir models. The study focuses on a Marcellus shale asset that includes 135 wells with multiple pads, different landing targets, well length and reservoir properties. The full field history matching process was successfully completed using this data driven approach thus capturing the production behavior with acceptable accuracy for individual wells and for the entire asset.Ó 2015, China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

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Section: Top-down Modeling E Pattern Recognition Based Reservoir Modementioning

confidence: 99%

Full field reservoir modeling of shale assets using advanced data-driven analytics

Esmaili

Mohaghegh

2016

Geoscience Frontiers

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“…By 2000, production rates increased somewhat to approximately 0.8 Bscf/D. However, in the subsequent decade, rates rapidly increased, and production rates amounted to 10 Bscf/ D in 2010 (Sutton et al 2010). The US Energy Information Administration forecasts that US shale-gas production will exceed 37 Bscf/D in 2035.…”

Section: Introductionmentioning

confidence: 98%

“…The US Energy Information Administration forecasts that US shale-gas production will exceed 37 Bscf/D in 2035. 1 The number of wells producing shale gas increased from 500 to more than 12,000 from 1995 to 2010 (Sutton et al 2010). Although the impact of unconventional gas in the US has been enormous, opinions are divided on the question as to what degree this success can be replicated outside the US (Martin 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Variations are observed on a regional scale (hundreds to thousands of square kilometers) as well as a local scale (tens to hundreds of square kilometers). Even within the best acreage of a play, 3 well performance was found to be very variable even if wells were drilled in sites with apparently similar geology and production characteristics (Wright 2008;Sutton et al 2010;LaFolette and Holcomb 2011;Strickland et al 2011). Consequently, geoscientists are generally able to delineate a play at a regional level.…”

Section: Introductionmentioning

confidence: 99%

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Combining Geostatistics With Bayesian Updating To Continually Optimize Drilling Strategy in Shale-Gas Plays

Willigers

Begg

Bratvold

2014

SPE Reservoir Evaluation &Amp; Engineering

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We present a new methodology to evaluate subsurface uncertainty during the development of shale-gas plays. Even after many wells are drilled, the average well production and the variation of well performance (economics) remain highly uncertain. The ability to delineate a shale play with the fewest wells and to focus drilling in the most-productive areas is a major factor in commercial success.The importance of probabilistic modeling in managing uncertainty in shale-gas plays is emphasized in several studies. The objective of this study is to develop a practical methodology that addresses these complexities and is dynamic, in the sense that the optimal drilling strategy can be continually updated as we learn the outcome of each well drilled.Maximizing the returns from a shale-gas play is essentially a problem of choosing well locations and number of wells to optimize production volumes and rates. Drilling policies must take account of many already-drilled locations, possible new drilling locations, spatial dependencies between performance at those different (possible) well locations, and the extent of uncertainty concerning whether a well will be economical. These factors cause typical valuation methodologies to be impractical because of the "curse of dimensionality."In this study, an unconventional play is divided into numerous segments. These segments are referred to as cells. In each cell, a fixed number of wells can be drilled. The chance of success (CoS) [of a well with a net present value (NPV) greater than zero] in any given cell is itself considered to be an uncertain variable. An initial probability distribution for the CoS of each cell is derived from analogous plays plus any available information about the specific play.The methodology proceeds as follows. First, as each new well (or group of new wells) is drilled, the outcome is used in combination with the prior probability distribution (with Bayes theorem) to create an updated probability distribution for the CoS of the relevant grid cell. Thus, our initial estimate can be continuously updated as more actual outcomes are realized. Second, the influence of the new CoS on the surrounding cells, because of spatial correlation, is updated by use of indicator Kriging (IK), a geostatistical technique. This combination of Bayesian update (BU) and IK is referred to as the BU-IK method.The methodology proposed in this study informs the development of drilling policies for shale-gas opportunities with a probabilistic model that accounts for the uncertainty in the CoS and its spatial dependency. The use of cells to represent a set of wells simplifies the analysis and greatly reduces the computing requirements. The methodology is applied to a well set from the Barnett shale in Texas.

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“…In the case of rate-transient analysis, formulations were not initially available to handle the physical geometry of the completions. Simulation is more flexible, but there is significant uncertainty as to the nature of the resulting completion, production mechanisms and how to model these [Strickland et al, 2011]. Studies have shown that Darcy flow and instantaneous capillary equilibrium applied in simulators for conventional reservoirs are inadequate for reliable simulation of shale gas reservoirs [Andrade et al, 2010].…”

Section: Problem Descriptionmentioning

confidence: 99%

Production History Matching and Forecasting of Shale Assets Using Pattern Recognition

Esmaili¹

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Generating long-term development plans and reservoir management of shale assets has continued apace. In this study, a novel method that integrates traditional reservoir engineering with pattern recognition capabilities of artificial intelligence and data mining is applied in order to accurately and efficiently model fluid flow in shale reservoirs. The methodology is efficient due to its relatively short development time and is accurate as a result of high quality history matches it achieves for individual wells in a multiwell asset. The technique that is named Artificial Intelligence (AI) Based Reservoir Modeling is a formalized and comprehensive, full-field empirical reservoir model. It integrates all aspects of shale reservoir development from well location and configuration to reservoir characteristics and to completion and hydraulic fracturing. This approach not only has a much faster turnaround time compared to the numerical simulation techniques, but also models the production from the field with good accuracy, incorporating all the available data. This integrated framework enables reservoir engineers to compare and contrast multiple scenarios and propose field development strategies. AI-based Modeling is applied to a Marcellus Shale asset that includes 135 horizontal wells from 43 pads with different landing targets. The full field AI-based Shale model is used for predicting the future well/reservoir performance, forecasting the behavior of new wells/pads and to assist in planning field development strategies. Furthermore, this study takes advantage of applying advanced pattern recognition tools in order to investigate the impact of design and native parameters on gas production as well as optimizing the completion and stimulation parameters for newly planned wells.

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Practical Aspects of Reserve Determinations for Shale Gas

Cited by 22 publications

References 19 publications

Full field reservoir modeling of shale assets using advanced data-driven analytics

Full field reservoir modeling of shale assets using advanced data-driven analytics

Combining Geostatistics With Bayesian Updating To Continually Optimize Drilling Strategy in Shale-Gas Plays

Production History Matching and Forecasting of Shale Assets Using Pattern Recognition

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