Methodological scheme for ranking interval expert estimates of the territories hydrocarbon potential

Keywords: expert interval-valued estimation, decision-making, distance metric, aggregation, prospects of oil and gas

Abstract

The problem of priorities establishing for expert interval-valued estimations when experts hold the opposite opinion is considered. The whole group of expert estimates is subdivided into subgroups, first of which provides the probability of the deposit presence, and the second one provides the probability of deposit missing. A ranking methodology for interval expert estimates of the territories’ hydrocarbon potential, consisting of two stages, is proposed. At the first stage, an estimates formed by two subgroups of experts are separately aggregated by optimization. Two aggregated interval estimates of the corresponding hypotheses probabilities are obtained as a result. In the second stage, a priority estimate is determined by comparing the results. A numerical example of the test territory evaluating for a hydrocarbon deposit presence was calculated. Interval-valued estimates by five experts were used in this example for the hypotheses of hydrocarbons presence/missing. Various metrics of the distance between interval values were used to calculate persistent minima of aggregating estimates. The results of the calculations indicate the hypothesis’ priority of a hydrocarbon deposit presence within the study area. The proposed methodology for ranking interval-valued expert estimates can be used in the “Geologist’s Computer Assistant” software system.The problem of priorities establishing for expert interval-valued estimations when experts hold the opposite opinion is considered. The whole group of expert estimates is subdivided into subgroups, first of which provides the probability of the deposit presence, and the second one provides the probability of deposit missing. A ranking methodology for interval expert estimates of the territories’ hydrocarbon potential, consisting of two stages, is proposed. At the first stage, an estimates formed by two subgroups of experts are separately aggregated by optimization. Two aggregated interval estimates of the corresponding hypotheses probabilities are obtained as a result. In the second stage, a priority estimate is determined by comparing the results. A numerical example of the test territory evaluating for a hydrocarbon deposit presence was calculated. Interval-valued estimates by five experts were used in this example for the hypotheses of hydrocarbons presence/missing. Various metrics of the distance between interval values were used to calculate persistent minima of aggregating estimates. The results of the calculations indicate the hypothesis’ priority of a hydrocarbon deposit presence within the study area. The proposed methodology for ranking interval-valued expert estimates can be used in the “Geologist’s Computer Assistant” software system.

References

Vencel E. S. (1969). Teoriya veroyatnostej. Moscow. Nauka. 576 p.

Mstislavskaya L. P., Filippov V. P. (2005). Geologiya, poiski i razvedka nefti i gaza. Moscow. CentrLitNefteGaz. 199 p.

Popov, M. A., Stankevich, S. A., Arkhipov, A. I., Titarenko, O. V. (2018) About possibility of hydrocarbon deposit remote detection using computer assistance. Ukrayinskij zhurnal distancijnogo zonduvannya Zemli. V. 16. 34-40.

URL: https://www.ujrs.org.ua/ujrs/article/view/119?source=/ujrs/article/view/119

Lyalko, V. I., Popov, M. O. (Eds) (2017) Novel remote sensing methods for minerals prospecting. Kyiv. NAS of Ukraine-CASRE. 221 p. CD.

Trofimov, D. M., Evdokimenkov, V. N., Shuvaeva, M. K. (2012). Sovremennye metody i algoritmy obrabotki kosmicheskoj, geologo-geofizicheskoj i geohimicheskoj informacii dlya prognoza uglevodorodnogo potenciala neizuchennyh uchastkov nedr. Moscow. Fizmatlit. 320 p.

Alefeld, G., Herzberger, J. (2012). Introduction to Interval Computations. NY: Academic Press, 352 p. DOI: 10.1016/C2009-0-21898-8.

Cavazzuti M. Optimization Methods: From Theory to Design. Berlin: Springer, (2013). 262 p. DOI: 10.1007/978-3-642-31187-1.

Chavent, M., De Carvalho, F.A.T., Lechevallier, Y., Verde, R. (2006). New clustering methods for interval data. Computational statistics. Vol. 21, №2. P. 211–229. DOI: 10.1007/s00180-006-0260-0.

Pankratova, N.D., Nedashkovskaya, N.I. (2016). Estimation of Decision Alternatives on the Basis of Interval Pairwise Comparison Matrices. Intelligent Control and Automation. Vol. 7, №2. P. 39-54. DOI: 10.4236/ica.2016.72005.

Tran, L., Duckstein, L. (2002). Comparison of fuzzy numbers using a fuzzy distance measure. Fuzzy Sets and Systems. Vol. 130, № 3. P. 331–341. DOI: 10.1016/S0165-0114(01)00195-6.

Xu, Z. (2013). Group decision-making model and approach based on interval preference orderings. Computers & Industrial Engineering. Vol. 64, №3. P. 797–803. DOI: 10.1016/j.cie.2012.12.013.

Xu, Z.S., Da, Q.L. (2002). The Uncertain OWA Operator. International Journal of Intelligent Systems. Vol. 17, №3. P. 569–575. DOI: 10.1002/int.10038.

Yuan, H., Qu, Y. (2015). Model for Conflict Resolution with Preference Represented as Interval Numbers. MATEC Web of Conferences. Publ. by EDP Sciences. Vol. 22. 3 p. DOI: 10.1051/matecconf/20152201030.

Section
Techniques for Earth observation data acquisition, processing and interpretation