Uncertainty Management: a structured Approach towards Recognizing, Quantifying and Managing Sampling Biases in Subsurface Unknowns

Alessio, Laurent; Everts, Arnout J.W.; Rahmat, Faeez

doi:10.29118/ipa.1260.10.o.002

Cited by 5 publications

(9 citation statements)

References 5 publications

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“…The interpretation can often be challenging (with extreme skin effect values recorded sometimes, attributable to number of causes [7]) and the resulting permeability distribution over a play may vary greatly. Authors generally agree that a trend of decreasing permeability with increase in depth is encountered in CBM reservoirs [4,6], However with the great localized variability the depth trend may not be visible if sufficiently large data base is not available ( Figure 6 show a sizeable permeability dataset which allows for identification of permeability-depth trend which may not be visible with a more limited number of data points). Contrasts in vertical permeability within a given well, as well as areally between adjacent neighbouring wells can be up to two to three (2-3) orders of magnitude.…”

Section: Permeability Modellingmentioning

confidence: 96%

“…: a given well) to another observation (a neighbouring well), it is important to understand the difference between variability and uncertainty [4]. Variability is defined as a short to medium scale (up to inter-well scale) variations of a given parameter, such as permeability, porosity, gas content (for CBM reservoirs), hydrocarbon saturation etc.…”

Section: Variability and Uncertaintymentioning

confidence: 99%

“…Variability is intrinsic and non-temporal. Ultimate understanding of variability often remains spacially poorly predictive [4]. Uncertainty is defined at the field, sector or segment (field unit) scale, where multiple wells will be ultimately drilled.…”

Section: Variability and Uncertaintymentioning

confidence: 99%

“…Development related metrics (e.g. field recoverable volumes), at a given time, are a consequence of the level of understanding of the subsurface and the concept development choices that were made [4]. Uncertainty and variability analysis should be carried out as part of reservoir characterization.…”

Section: Variability and Uncertaintymentioning

confidence: 99%

“…Fundamentals of reservoir characterization, production forecasting and development planning according to our workflow are discussed in this paper. We believe (and we are trying to demonstrate in this paper) that the main ingredients for successful CBM field development planning are: -necessity for adequate characterization of variability vs. uncertainty [4], -rigorous and repeatable workflow that utilizes all relevant data, -introduction of metrics that allow for identification of the influence of subsurface parameters and development decisions on the value of a project.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Methodologies and Tools for Coalbed Methane (CBM) Field Development Planning Studies

Ryba¹,

Everts²,

Alessio³

2011

SPE Asia Pacific Oil and Gas Conference and Exhibition

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Resource plays such as Coalbed Methane (also called Coal Seam Gas) fields typically exhibit a number of unique characteristics that set them apart to other hydrocarbon accumulations. We will argue in this paper that as consequence, conducting effective field development planning studies for CBM resources requires adopting a markedly different approach to the ones typically followed with conventional oil and gas fields. In contrast to traditional offshore plays, replete with a rich literature of best practices and adequate tools, coalbed methane field development planning typically involves very large areas and number of wells, hence a correlative abundance of (static and dynamic) data, but also displays an often surprising variability between neighbouring wells. This variability is the consequence of a high degree of heterogeneity present at a small (1's-10's metres) to medium (10's-100's meters) scale, notably for permeability, but also isothermal properties, gas content, and even net coal distribution and seam geometry for some plays. This paper offers insights on integrated development planning methods, uncertainty management techniques and modelling workflows, accumulated through the experience of a number of CBM plays. Expected relationships and possible crossparameter models for the key reservoir properties are discussed. The development of practical tools to characterise the reservoir properties, as well as the performance of different well and completion configurations, simulation, field development planning, all the way to ultimately assessing the economic potential of the play are also illustrated.

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Section: Permeability Modellingmentioning

confidence: 96%

Section: Variability and Uncertaintymentioning

confidence: 99%

Section: Variability and Uncertaintymentioning

confidence: 99%

Section: Variability and Uncertaintymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Methodologies and Tools for Coalbed Methane (CBM) Field Development Planning Studies

Ryba¹,

Everts²,

Alessio³

2011

SPE Asia Pacific Oil and Gas Conference and Exhibition

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Subsurface high-grading potential in Coal Seam Gas in eastern Australia

Everts,

Alessio,

Connell

2023

Australian Journal of Earth Sciences

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Multi-Realisation Full-Field History-Matching For CBM Resources a New Approach with Global Optimisation Coupled with Geo-Statistical Filtering

Alessio

Nasri

Pan

et al. 2013

SPE Asia Pacific Oil and Gas Conference and Exhibition

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CBM reservoirs are known to exhibit a large degree of variability in production characteristics and performance at the interwell space. This is a consequence of the variable nature of the fractured permeability network, but also other important measurable parameters such as gas content, isotherms, and much more difficult to measure ones such as porosity, relative permeability and matrix shrinkage.Once the field is on production for a period of time, operators often wish to perform a history-match of the asset, to determine the remaining production potential, and the most adequate future development activities. Traditionally, this exercise is conducted with numerical models.An unfortunate reality of history matching is the existence of multiple solutions, which will behave differently when forecasted in the future. This is a consequence of the fact that multiple parameters influence the match and their interdependency produces effects that are may compensate each other; many of these parameters are generally either poorly understood and/or highly variable at the inter-well scale. In addition, most of the critical parameters for dynamic performance are not measured in well logs, nor seismic (which often is not available) -only through more expensive activities such as coring and production testing.Given this reality, using the classical conventional gas workflows for the implementation of 3D history matched models for CBM fields is shown to be inefficient since the transition from the reservoir characterization to the static modelling is insufficiently constrained, and doesn't include the production performance of the existing wells.In order to adapt the classical workflows to the complex case of CBM fields, we introduce in this paper a filtering process based on geo-statistical constraints which uses the single well stochastic history matches to generate field matches and invest the matched drainage areas on the generation of "average properties" maps. These maps can thereafter be used as input into the infill drilling analysis and the resource assessment studies, but also introduced into the static model to condition the property model, and achieve a field history-match which much more closely reproduces the production behavior of individual wells. .

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Uncertainty Management: a structured Approach towards Recognizing, Quantifying and Managing Sampling Biases in Subsurface Unknowns

Cited by 5 publications

References 5 publications

Methodologies and Tools for Coalbed Methane (CBM) Field Development Planning Studies

Methodologies and Tools for Coalbed Methane (CBM) Field Development Planning Studies

Subsurface high-grading potential in Coal Seam Gas in eastern Australia

Multi-Realisation Full-Field History-Matching For CBM Resources a New Approach with Global Optimisation Coupled with Geo-Statistical Filtering

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