Efficacy of Information in Mineral Exploration Drilling

Caers, Jef; Scheidt, Céline; Yin, Zhen Yu; Wang, Lijing; Mukerji, Tapan; House, K.

doi:10.1007/s11053-022-10030-1

Cited by 11 publications

(7 citation statements)

References 34 publications

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“…The idea of VOI was applied and developed in works such as Liu et al (2012) to the remediation of groundwater sites, Trainor-Guitton et al ( 2013) to monitor the detection of CO 2 leaks, Nenna and Knight (2014) to evaluate the benefits of acquiring geophysical data as part of a groundwater management strategy, and a geophysical perspective on VOI is presented by Trainor-Guitton (2014). In addition, a new concept called efficacy of information (EOI), which is similar to VOI but without financial rewards or costs, was recently introduced by Caers et al (2022). Our approach does not take into account the financial or logistical costs of the experiment, but instead focuses on the information gain from the experiment; however, decision-theoretic metrics can easily be incorporated into our framework into a VOI or EOI framework, because information gain is the first stage of the VOI framework.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, a new concept called efficacy of information (EOI), which is similar to VOI but without financial rewards or costs, was recently introduced by Caers et al. (2022). Our approach does not take into account the financial or logistical costs of the experiment, but instead focuses on the information gain from the experiment; however, decision‐theoretic metrics can easily be incorporated into our framework into a VOI or EOI framework, because information gain is the first stage of the VOI framework.…”

Section: Discussionmentioning

confidence: 99%

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Comparing Well and Geophysical Data for Temperature Monitoring Within a Bayesian Experimental Design Framework

Thibaut

Compaire

Lesparre

et al. 2022

Water Resources Research

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Geothermal systems, including borehole thermal energy storage (BTES) and shallow aquifer thermal energy storage systems (ATES) are becoming more popular as the world looks for ways to reduce greenhouse gas emissions. Such systems use thermal energy extracted from the ground or groundwater to heat or cool buildings, which necessitates some electrical energy input for the heat pump, while storing the excess heat or cold underground. The goal is to re-use this thermal energy during the next season in a cyclic utilization (Bayer et al., 2013;Duijff et al., 2021;Saner et al., 2010;Vanhoudt et al., 2011). The performance of BTES and ATES strongly depends on the subsurface properties. Many variables are involved in geothermal processes, including porosity, hydraulic conductivity, thermal conductivity, and heat capacity. Subsurface temperature fluctuations are strongly influenced by the spatial distribution of these parameters, the boundary conditions, and the aquifer's hydraulic gradient

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Comparing Well and Geophysical Data for Temperature Monitoring Within a Bayesian Experimental Design Framework

Thibaut

Compaire

Lesparre

et al. 2022

Water Resources Research

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“…Optimal placement of drill holes for mineral exploration and mining (resource delineation) has received significant attention. Some methodologies aim to minimize the uncertainty in spatial properties through use of geostatistical algorithms that model the effect of measured data on spatial uncertainty (Pilger et al, 2001;Koppe et al, 2011Koppe et al, , 2017Caers et al, 2022;Hall et al, 2022). Others rely on decision theoretic concepts of value of information to quantify the dollar value of gathered information to reduce uncertainty in an economic property of interest (Froyland et al, 2004;Eidsvik and Ellefmo, 2013;Soltani-Mohammadi and Hezarkhani, 2013).…”

Section: Introductionmentioning

confidence: 99%

The Intelligent Prospector v1.0: geoscientific model development and prediction by sequential data acquisition planning with application to mineral exploration

Mern¹,

Caers

2023

Geosci. Model Dev.

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Abstract. Geoscientific models are based on geoscientific data; hence, building better models, in the sense of attaining better predictions, often means acquiring additional data. In decision theory, questions of what additional data are expected to best improve predictions and decisions is within the realm of value of information and Bayesian optimal survey design. However, these approaches often evaluate the optimality of one additional data acquisition campaign at a time. In many real settings, certainly in those related to the exploration of Earth resources, a large sequence of data acquisition campaigns possibly needs to be planned. Geoscientific data acquisition can be expensive and time-consuming, requiring effective measurement campaign planning to optimally allocate resources. Each measurement in a data acquisition sequence has the potential to inform where best to take the following measurements; however, directly optimizing a closed-loop measurement sequence requires solving an intractable combinatoric search problem. In this work, we formulate the sequential geoscientific data acquisition problem as a partially observable Markov decision process (POMDP). We then present methodologies to solve the sequential problem using Monte Carlo planning methods. We demonstrate the effectiveness of the proposed approach on a simple 2D synthetic exploration problem. Tests show that the proposed sequential approach is significantly more effective at reducing uncertainty than conventional methods. Although our approach is discussed in the context of mineral resource exploration, it likely has bearing on other types of geoscientific model questions.

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“…The investigation of the earth's subsurface to a large extent; can only be done through drilling before the coming of geophysics. The drilling process is time-consuming, highly expensive, tedious, and destructive [1,2,3]. However, geophysics investigation by drilling appears more certain and accurate, but it is highly limited because it can only provide subsurface information at a discrete location, and it is highly restricted to a certain terrain [4,5,6,1].…”

Section: Introductionmentioning

confidence: 99%

Modification of the Traditional Half Schlumberger Arrays and its Efficiency for Subsurface Exploration

Abdullahi,

Nagoda,

Jaiyeoba

et al. 2023

Preprint

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Geophysical investigation of the earth’s subsurface is vital for engineering and environmental surveys. Application of the basic methods of geophysics to detect and study subsurface anomalies for engineering and environmental problems. This study modified the existing Half Schlumberger arrays after evaluating the efficiency of the existing Half Schlumberger array. The Modified Half Schlumberger (SMH) relatively produces a better result for a deeper geophysical investigation where there is a limited space than the existing Half Schlumberger (SH). The results of the investigated nine (9) VES stations were presented in tables and graphs, which shows that the two arrays are nearly the same (about 94% in similarity). The SMH array shows an interesting result. It was noted that the S_MH probes were deeper than the usual SF arrays with an average extension of about 2.4 m depth in terms of overburden thickness. In addition, the two arrays produce identical curve types, subsurface parameters, and soil depth section sequences across all the VES stations. The identical results confirmed the efficiency and reliability of the SMH array. Consequently, SMH can be adopted as a practicable alternative array to the traditional SH and the conventional Full Schlumberger (SF) array for any geophysical survey, where the usual SF array is applicable, most especially when deeper subsurface information is required and the available space is highly limited.

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Efficacy of Information in Mineral Exploration Drilling

Cited by 11 publications

References 34 publications

Comparing Well and Geophysical Data for Temperature Monitoring Within a Bayesian Experimental Design Framework

Comparing Well and Geophysical Data for Temperature Monitoring Within a Bayesian Experimental Design Framework

The Intelligent Prospector v1.0: geoscientific model development and prediction by sequential data acquisition planning with application to mineral exploration

Modification of the Traditional Half Schlumberger Arrays and its Efficiency for Subsurface Exploration

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