Dynamic exploration designs for graphical models using clustering with applications to petroleum exploration

Martinelli, Gabriele; Eidsvik, Jo

doi:10.1016/j.knosys.2013.08.020

Cited by 11 publications

(7 citation statements)

References 29 publications

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“…When using the range of 5 cells, the proportion of neighboring facies type is lower as the sampling area has a lower overlap with the neighbor facies type in the concept models (see Figs. 4,5,6,7,8). Increasing the search range from 5 to 10 adds more spatial uncertainty, systematically increasing the entropy and reducing the prior EV of all deriving JDPs.…”

Section: Results Analysismentioning

confidence: 99%

“…During the interactive process of creating the JPD, we observed that the maps from Figs. 4,5,6,7,8, and the marginal distributions, are good overall indicators of how well the JPD captures the experts' expectations considering the chosen parameters.…”

Section: Applicationmentioning

confidence: 99%

“…The sequential drilling problem has been discussed in different contexts by several authors, e.g., [1][2][3][4][5][6][7][8]. The main goal is to create value by identifying the optimal drilling policy and proceed accordingly.…”

Section: Introductionmentioning

confidence: 99%

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Probability elicitation using geostatistics in hydrocarbon exploration

Morosov

Bratvold

2021

Comput Geosci

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The exploratory phase of a hydrocarbon field is a period when decision-supporting information is scarce while the drilling stakes are high. Each new prospect drilled brings more knowledge about the area and might reveal reserves, hence choosing such prospect is essential for value creation. Drilling decisions must be made under uncertainty as the available geological information is limited and probability elicitation from geoscience experts is key in this process. This work proposes a novel use of geostatistics to help experts elicit geological probabilities more objectively, especially useful during the exploratory phase. The approach is simpler, more consistent with geologic knowledge, more comfortable for geoscientists to use and, more comprehensive for decision-makers to follow when compared to traditional methods. It is also flexible by working with any amount and type of information available. The workflow takes as input conceptual models describing the geology and uses geostatistics to generate spatial variability of geological properties in the vicinity of potential drilling prospects. The output is stochastic realizations which are processed into a joint probability distribution (JPD) containing all conditional probabilities of the process. Input models are interactively changed until the JPD satisfactory represents the expert’s beliefs. A 2D, yet realistic, implementation of the workflow is used as a proof of concept, demonstrating that even simple modeling might suffice for decision-making support. Derivative versions of the JPD are created and their effect on the decision process of selecting the drilling sequence is assessed. The findings from the method application suggest ways to define the input parameters by observing how they affect the JPD and the decision process.

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Section: Results Analysismentioning

confidence: 99%

Section: Applicationmentioning

confidence: 99%

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Probability elicitation using geostatistics in hydrocarbon exploration

Morosov

Bratvold

2021

Comput Geosci

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“…DEFINITION 6 (Multi-instance Graphical Clustering [17]): Assume that dynamic programming is set up for each cluster, given the current evidence. By considering a variation of (Eq.…”

Section: Clustering Strategies Of Multiple Clusters Updatementioning

confidence: 99%

Multi-instance graphical transfer clustering for traffic data learning

Xue

et al. 2016

2016 International Joint Conference on Neural Networks (IJCNN)

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In order to better model complex real-world data and to develop robust features that capture relevant information, we usually employ unsupervised feature learning to learn a layer of features representations from unlabeled data. However, developing domain-specific features for each task is expensive, time-consuming and requires expertise of the data. In this paper, we introduce multi-instance clustering and graphical learning to unsupervised transfer learning. For a better clustering efficient, we proposed a set of algorithms on the application of traffic data learning, instance feature representation, distance calculation of multi-instance clustering, multi-instance graphical cluster initialisation, multi-instance multi-cluster update, and graphical multi-instance transfer clustering (GMITC). In the end of this paper, we examine the proposed algorithms on the Eastwest datasets by couples of baselines. The experiment results indicate that our proposed algorithms can get higher clustering accuracy and much higher programming speed.

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“…Sequential optimization problems are typically solved using dynamic programming (Bellman, 1957;Puterman, 2005), but the 'curse of dimensionality' that is encountered in larger problems requires computational techniques for approximate solutions (Powell, 2011). Some of these methods have been deployed for solving sequential decision problems in applications with dependence, including models such as graphical models or Bayesian networks (BNs) (Krause and Guestrin, 2009;Brown and Smith, 2013;Martinelli et al, 2013a), Markov random fields (Bonneau et al, 2014;Martinelli and Eidsvik, 2014) and Gaussian processes (GPs) (Srinivas et al, 2010). In the rock hazard application, we compare some common heuristics for sequential information gathering.…”

Section: Introductionmentioning

confidence: 99%

Sequential information gathering schemes for spatial risk and decision analysis applications

Eidsvik¹,

Martinelli²,

Bhattacharjya

2017

Stoch Environ Res Risk Assess

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Several risk and decision analysis applications are characterized by spatial elements: there are spatially dependent uncertain variables of interest, decisions are made at spatial locations, and there are opportunities for spatial data acquisition. Spatial dependence implies that the data gathered at one coordinate could inform and assist a decision maker at other locations as well, and one should account for this learning effect when analyzing and comparing information gathering schemes. In this paper, we present concepts and methods for evaluating sequential information gathering schemes in spatial decision situations. Static and sequential information gathering schemes are outlined using the decision theoretic notion of value of information, and we use heuristics for approximating the value of sequential information in large-size spatial applications. We illustrate the concepts using a Bayesian network example motivated from risks associated with CO2 sequestration. We present a case study from mining where there are risks of rock hazard in the tunnels, and information about the spatial distribution of joints in the rocks may lead to a better allocation of resources for choosing rock reinforcement locations. In this application, the spatial variables are modeled by a Gaussian process. In both examples there can be large values associated with adaptive information gathering.

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Dynamic exploration designs for graphical models using clustering with applications to petroleum exploration

Cited by 11 publications

References 29 publications

Probability elicitation using geostatistics in hydrocarbon exploration

Probability elicitation using geostatistics in hydrocarbon exploration

Multi-instance graphical transfer clustering for traffic data learning

Sequential information gathering schemes for spatial risk and decision analysis applications

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