A holistic approach to assessment of value of information (VOI) with fuzzy data and decision criteria

Martin Vilela; Gbenga Oluyemi; Andrei Petrovski

doi:10.31181/dmame2003097v

Authors

Martin Vilela School of Engineering, Robert Gordon University, Aberdeen, United Kingdom
Gbenga Oluyemi School of Computing, Robert Gordon University, Aberdeen, United Kingdom
Andrei Petrovski School of Computing, Robert Gordon University, Aberdeen, United Kingdom

DOI:

https://doi.org/10.31181/dmame2003097v

Keywords:

Value of information, fuzzy logic, design of experiments, uncertainty, decision making

Abstract

Classical decision and value of information theories have been applied in the oil and gas industry from the 1960s with partial success. In this research, we identify that the classical theory of value of information has weaknesses related with optimal data acquisition selection, data fuzziness and fuzzy decision criteria and we propose a modification in the theory to fill the gaps found. The research presented in this paper integrates theories and techniques from statistical analysis and artificial intelligence to develop a more coherent, robust and complete methodology for assessing the value of acquiring new information in the context of the oil and gas industry. The proposed methodology is applied to a case study describing a value of information assessment in an oil field where two alternatives for data acquisition are discussed. It is shown that: i) the technique of design of experiments provides a full identification of the input parameters affecting the value of the project and allows a proper selection of the data acquisition actions, ii) when the fuzziness of the data is included in the assessment, the value of the data decreases compared with the case where data are assumed to be crisp; this result means that the decision concerning the value of acquiring new data depends on whether the fuzzy nature of the data is included in the assessment and on the difference between the project value with and without data acquisition, iii) the fuzzy inference system developed for this case study successfully follows the logic of the decision-maker and results in a straightforward system to aggregate decision criteria.

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References

Ahmed, A., Elkatatny, S., Ali, A., Mahmoud, M. & Abdulraheem, A. (2019). Rate of penetration prediction in shale formation using fuzzy logic. In: Proceedings of International Petroleum Technology Conference, 26–29 March 2019. Beijing, China: Society of Petroleum Engineers. SPE 19548-MS.

Alloush, R., Elkatatny, S., Mahmoud, M., Moussa, T., Ali, A. & Abdulraheem, A. (2017). Estimation of geomechanical failure parameters from well logs using artificial intelligence techniques. In: Proceedings of Kuwait Oil & Gas Show and Conference, 15–18 October 2017. Kuwait City, Kuwait: Society of Petroleum Engineers. SPE 187625-MS.

Assilian, S. & Mamdani, E. (1974). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7, pp. 1–13. Reprint: International Journal of Human-Computer Studies, 1999, 51, pp. 135–147.

Bandler, W. & Kohout, L. (1978). Fuzzy relational products and fuzzy implication operators. In: Proceedings of International Workshop on Fuzzy Reasoning Theory and Applications. September 1978. London, UK. Queen Mary College, University of London.

Basfar, S., Baarimah, S., Elkatany, S., Al-Ameri, W., Zidan, K. & Al-Dogail, A. (2018). Using artificial intelligence to predict IPR for vertical oil well in solution gas drive reservoirs: A new approach. In: Proceedings of Annual Technical Symposium and Exhibition, 23–26 April 2018. Dammam, Saudi Arabia, Kingdom of Saudi Arabia: Society of Petroleum Engineers. SPE 192203-MS.

Bellman, R. & Zadeh, L. (1970). Decision-making in a fuzzy environment. Management Science, 17(4), pp. B-141–B-164.

Bezdek, J. (1993). Fuzzy models—what are they, and why? IEEE Transactions on Fuzzy Logic Systems, 1(1), pp. 1–6.

Bezdek, J. (2014). Using fuzzy logic in business. Procedia - Social and Behavioral Sciences, 124, pp. 371–380.

Box, G. & Draper, N. (1987). Empirical model-building and response surfaces. New York, USA: John Wiley & Sons.

Box, G., Hunter, W. & Hunter, J. (1978). Statistics for experimenters. An introduction to design, data analysis and model building, 2nd ed. Hoboken, New Jersey, USA: John Wiley & Sons.

Box, G.E.P. & Wilson, K.B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13, pp. 1–45.

Bratvold, R., Bickel, J., & Lohne, H. (2007). Value of information in the oil and gas industry: Past, present, and future. In: Proceedings of Annual Technical Conference and Exhibition. 11–14 November 2007. Anaheim, California, USA: Society of Petroleum Engineers. SPE 110378-MS.

Bukhamseen, N., Al-Najem, A., Saffar, A. & Ganis, S. (2017). An injection optimization decision-making tool using streamline based fuzzy logic workflow. In: Proceedings of Reservoir Characterization Conference and Exhibition, 8–10 May 2017. Abu Dhabi, UAE: Society of Petroleum Engineers. SPE 186021-MS.

Cervantes, M.J., & Engstrom, T.F. (2004). Factorial design applied to CFD. Journal of Fluids Engineering, 126(5), pp. 791–798.

Chattopadhyay, S. (2014). A neuro-fuzzy approach for the diagnosis of depression. Applied Computing and Informatics (2017) 13, pp. 10–18.

Corre, B., de Feraudy, V. & Vincent, G. (2000). Integrated uncertainty assessment for project evaluation and risk analysis. In: Proceedings of European Petroleum Conference. 24–25 October 2000. Paris, France: Society of Petroleum Engineers. SPE 65205-MS.

Damsleth, E., Hage, A., & Volden, R. (1992). Maximum information at minimum cost: A North Sea field development study with an experimental design. In: Proceedings of Offshore Europe Conference. 3–6 September 1992. Aberdeen, UK: Society of Petroleum Engineers. SPE 23139-PA.

Dejean, J. & Blanc, G. (1999). Managing uncertainties on production predictions using integrated statistical methods. In: Proceedings of Annual Technical Conference and Exhibition. 3–6 October 1999. Houston, Texas, USA: Society of Petroleum Engineers. SPE 56696-MS.

Demirmen, F. (1996). Use of “value of information” concept in justification and ranking of subsurface appraisal. In: Proceedings of Annual Technical Conference and Exhibition. 6–8 October 1996. Denver, USA: Society of Petroleum Engineers. SPE 36631-MS.

Dougherty, E. (1971). The oilman’s primer on statistical decision theory. Society of Petroleum Engineers Library (unpublished). SPE 3278-MS.

Du, Z., Yang, J., Yao, Z. & Xue, B. (2002). Modeling approach of regression orthogonal experiment design for the thermal error compensation of a CNC turning center. Journal of Materials Processing Technology, 129(1–3), pp. 619–623.

Dunn, M. (1992). A method to estimate the value of well log information. In: Proceedings of Annual Technical Conference and Exhibition. 4–7 October 1992. Washington, D.C., USA: Society of Petroleum Engineers. SPE 24672-MS.

Egeland, T., Hatlebakk, E., Holden, L. & Larsen, E. (1992). Designing better decisions. In: Proceedings of European Petroleum Computer Conference. 25–27 May 1992. Stavanger, Norway: Society of Petroleum Engineers. SPE 24275-MS.

Farhang-Mehr, A. & Azann, S. (2005). Bayesian meta-modeling of engineering design simulations: A sequential approach with adaptation to irregularities in the response behavior. International Journal for Numerical Methods in Engineering, 62(15), pp. 2104–2126.

Fisher, R. (1973). Statistical methods and scientific inference, 3rd ed. New York, USA: Macmillan.

Friedmann, F., Chawathe, A., & Larue, D. (2001). Assessing uncertainties in channelized reservoirs using experimental design. In: Proceedings of Annual Technical Conference and Exhibition. 30 September–3 October 2001. New Orleans, Louisiana, USA: Society of Petroleum Engineers. SPE 71622-MS.

Galantucci, L., Percoco, G., & Spina, R. (2003). Evaluation of rapid prototypes obtained from reverse engineering. Proceedings of the Institution of Mechanical Engineers Part B: Journal of Engineering Manufacture, 217(11), pp. 1543–1552.

Gerhardt, J. & Haldorsen, H. (1989). On the value of information. In: Proceedings of Offshore Europe 89. Aberdeen, UK: Society of Petroleum Engineers. SPE 19291-MS.

Ghasem, N. (2006). Design of a fuzzy logic controller for regulating the temperature in industrial polyethylene fluidized bed reactor. Chemical Engineering Research and Design, 84(2), pp. 97‒106.

Goguen, J. (1967). L-fuzzy sets. Journal of Mathematical Analysis and Applications, 18, pp. 145–174.

Gou, Y. & Ling, J. (2008). Fuzzy Bayesian conditional probability model and its application in differential diagnosis of non-toxic thyropathy. In: 2008 2nd International Conference on Bioinformatics and Biomedical Engineering, Shanghai, IEEE, pp. 1843–1846

Grayson, C.J. (1960). Decisions under uncertainty. Drilling decisions by oil and gas operators. Boston, Massachusetts, USA: Harvard University.

Gupta, N., Abhinav, K. & Basava, A. (2011). Fuzzy file management. In: 2011 3rd International Conference on Electronic Computer Technology (ICET 2011), vol. 1, pp. 225–228.

Hoipkemeier-Wilson, L., Schumacher, J., Carman, M., Gibson, A., Feinberg, A., Callow, E., Finlay, J., Callow, J. & Brennan, A. (2004). Antifouling potential of lubricious, micro-engineered, PDMS elastomers against zoospores of the green fouling alga Ulva (Enteromorpha). Biofouling, 20(1), pp. 53–63.

Holmblad, L. P., & Østergaard, J. J. (1993). Control of a cement kiln by fuzzy logic. In Readings in fuzzy sets for intelligent systems (pp. 337-347). Morgan Kaufmann.

Jamshidi, A., Yazdani-Chamzini, A. & Yakhchali, S. (2013). Developing a new fuzzy inference system for pipeline risk assessment. Journal of Loss Prevention in the Process Industries, 26, pp. 197‒208.

Jayawardena, A., Perera, E., Zhu, B., Amarasekara, J., & Vereivalu, V. (2014). A comparative study of fuzzy logic systems approach for river discharge prediction. Journal of Hydrology, 514, pp. 85‒101.

Koninx, J. (2000). Value-of-information. From cost-cutting to value-creation. In: Proceedings of Asia Pacific Oil Conference and Exhibition. 16–18 October 2000. Brisbane, Australia: Society of Petroleum Engineers. SPE 64390-MS.

Kullawan, K., Bratvold, R. & Bickel, J. (2014). Value creation with multi-criteria decision making in geosteering operations. In: Proceedings of Hydrocarbon Economics and Evaluation Symposium. 19–20 May 2014. Houston, Texas, USA: Society of Petroleum Engineers. SPE 169849-MS.

Lakoff, G. (1978). Some remarks on AI and linguistics. Cognitive Science, 2, pp. 267–275.

Larsen, E., Kristoffersen, S. & Egeland, T. (1994). Functional integration in the design and use of a computer-based system for design of statistical experiments. In: Proceedings of European Petroleum Computer Conference. 15–17 March 1994. Aberdeen, Scotland, UK: Society of Petroleum Engineers. SPE 27585-MS.

Larsen, P. (1980). Industrial application of fuzzy logic control. International Journal of Man-Machine Studies, 12, pp. 3–10.

Law, A. & Kelton, W. (1991). Simulation modeling and analysis, 2nd ed. New York, USA: McGraw-Hill.

Law, A. (2015). Simulation modeling and analysis, 5th ed. New York, USA: McGraw-Hill.

Law, A. (2017). A tutorial on the design of experiments for simulation modeling. In: Proceedings of the 2017 Winter Simulation Conference, Savanah, GA, IEEE, pp. 550–564.

Liao, H.C. (2003). Using PCR-TOPS1S to optimize Taguchi’s multi-response problem. International Journal of Advanced Manufacturing Technology, 22(9–10), pp. 649–655.

Lohrenz, J. (1988). Net values of our information. Journal of Petroleum Technology, 40(4), pp. 499–503. SPE 16842-PA.

Mizumoto, M. & Tanaka, K. (1976). Some properties of fuzzy sets of type 2. Information and Control, 31, pp. 312–340.

Mizumoto, M. & Tanaka, K. (1981). Fuzzy sets and their operations. Information and Control, 48, pp. 30–48.

Montgomery, D.C. (2005). Design and analysis of experiments, 6th ed. New York, USA: John Wiley & Sons.

Moras, R., Lesso, W. & MacDonald, R. (1987). Assessing the value of information provided by observation wells in gas storage reservoirs. Society of Petroleum Engineers, Library (unpublished). SPE 17262-MS.

Muduli, L., Jana, P. & Prasad, D. (2018). Wireless sensor network-based fire monitoring in underground coal mines: A fuzzy logic approach. Process Safety and Environmental Protection, 113, pp. 435‒447.

Murtha, J., Osorio, R., Perez, H., Kramer, D., Skinner, R., & Williams, C. (2009). Experimental design: three contrasting projects. In: Proceedings of Latin American and Caribbean Petroleum Engineering Conference. 31 May–3 June 2009. Cartagena, Colombia: Society of Petroleum Engineers. SPE 121878-MS.

Musayev, A., Madatova, Sh. & Rustamov, S. (2016). Evaluation of the impact of the tax legislation reforms on the tax potential by fuzzy inference method. Procedia Computer Science, 102, pp. 507‒514.

Myers, R. & Montgomery, D. (2002). Response surface methodology. Process and product optimization using designed experiments. New York, USA: John Wiley & Sons.

Nataraj, M., Arunachalam, V.P. & Dhandapani, N. (2005). Optimizing diesel engine parameters for low emissions using Taguchi method variation risk analysis approach. Part 1. Indian Journal of Engineering and Materials Sciences, 12(3), pp. 169–181.

Negoita, C. & Ralescu, D.S. (1977). Applications of fuzzy sets to systems analysis. Basel: Birkhäuser.

Newendorp, P. (1967). Application of utility theory to drilling investment decisions. Ph.D. Thesis. The University of Oklahoma, USA.

Newendorp, P. (1972). Bayesian analysis — A method for updating risk estimates. Journal of Petroleum Technology, 24(2), pp. 193–198. SPE 3263-PA.

Ocampo, W. (2008). On the development of decision-making systems based on fuzzy models to assess water quality in rivers. Ph.D. thesis, Universitat Rovira I Virgili, Italy.

Ogle, T. & Hornberger, L. (2001). Technical note: Reduction of measurement variation: Small acoustic chamber screening of hard disk drives. Noise Control Engineering Journal, 49(2), pp. 103–107.

Oluwajuwon, I. & Olugbenga, F. (2018). Evaluation of water injection performance in heterogeneous reservoirs using analytical hierarchical processing and fuzzy logic. In: Proceedings of Nigerian Annual International Conference and Exhibition, 6–8 August 2018. Lagos, Nigeria: Society of Petroleum Engineers. SPE 193386-MS.

Pappis, C. & Mamdani, E. (1997). A fuzzy logic controller for a traffic junction. IEEE Transactions on Systems, Man and Cybernetics, 7(10), pp. 707–717.

Passmore, M.A., Patel, A. & Lorentzen, R. (2001). The influence of engine demand map design on vehicle perceived performance. International Journal of Vehicle Design, 26(5), pp. 509–522.

Peake, W., Abadah, M. & Skander, L. (2005). Uncertainty assessment using experimental design: Minagish Oolite Reservoir. In: Proceedings of Reservoir Simulation Symposium. 31 January–2 February 2005. Houston, Texas, USA: Society of Petroleum Engineers. SPE 91820-MS.

Popa, A. (2013). Identification of horizontal well placement using fuzzy logic. In: Proceedings of Annual Technical Conference and Exhibition, 30 September–2 October 2013. New Orleans, Louisiana, USA: Society of Petroleum Engineers. SPE 166313-MS.

Raiffa, H. & Schlaifer, R. (1961). Applied statistical decision theory. Boston, Massachusetts, USA: Harvard University.

Raiffa, H. (1968). Decision analysis: Introductory lectures on choices under uncertainty. Reading, Massachusetts, USA: Addison-Wesley.

Ruotolo, L.A.M. & Gubulin, J.C. (2005). A factorial-design study of the variables affecting the electrochemical reduction of Cr (VI) at polyaniline-modified electrodes. Chemical Engineering Journal, 110(1–3), pp. 113–121.

Sacks, J., Welch, W., Mitchell, T. & Wynn, H. (1989). Design and analysis of computer experiments. Statistical Science, 4(4), pp. 409–435.

Sakalli, M., Yan, H. & Fu, A. (1999). A fuzzy Bayesian approach to image expansion. In: IJCNN 1999, International Joint Conference on Neural Networks, IEEE, Washington, DC, USA, pp. 2685‒2689.

Sari, M. (2016). Estimating strength of rock masses using a fuzzy inference system. In: Rock Mechanics and Rock Engineering: From the past to the future. Taylor & Francis Group, London

Schlaifer, R. (1959). Analysis of decisions under uncertainty. New York, USA: McGraw-Hill.

Silbergh, M. & Brons, F. (1972). Profitability analysis — Where are we now? Journal of Petroleum Technology, 24(1), pp. 90–100. In: Proceedings of 45th Annual Fall Meeting. 4–7 October 1972. Houston, USA: Society of Petroleum Technology. SPE 2994-PA.

Sjoblom, J., Papadakis, K., Creaser, D., & Odenbrand, I. (2005). Use of experimental design in the development of a catalyst system. Catalysis Today, 100(3–4), pp. 243–248.

Sonmez, H., Gokceoglu, C., & Ulusay, R. (2004). A Mandani fuzzy inference system for the geological strength index (GSI) and its use in slope stability assessment. International Journal of Rock Mechanics and Mineral Science, 41(3), pp. 780‒785.

Stibolt, R. & Lehman, J. (1993). The value of a seismic option. In: Proceedings of Hydrocarbons Economics and Evaluation Symposium. 29–30 March 1993. Dallas, Texas, USA: Society of Petroleum Engineers. SPE 25821-MS.

Suffield, R.M, Dillman, S.H. & Haworth, J.E. (2004). Evaluation of antioxidant performance of a natural product in polyolefins. Journal of Vinyl and Additive Technology, 10(1), pp. 52–56.

Sugeno, M. & Kang, G. (1988). Fuzzy modeling and control of multilayer incinerator. Fuzzy Sets and Systems, 25, pp. 259–260.

Sugeno, M. & Murofushi, T. (1987). Pseudo-additive measures and integrals. Journal of Mathematics Analysis and Applications, 122, pp. 197–222.

Tanaka, K., Taniguchi, T., & Wang, H. (1999). Robust and optimal fuzzy control: A linear matrix inequality approach. In: Proceedings of 14th Triennial International Federation of Automatic Control (IFAC) World Congress. 5–9 July 1999. Beijing, P.R. China, pp. 5380–5385.

Telford, J. (2007). A brief introduction to design of experiments. Johns Hopkins APL Technical Digest, 27(3), pp. 224–232.

Tong, K.W., Kwong, C.K. & Yu, K.M. (2004). Process optimization of transfer moulding for electronic packages using artificial neural networks and multi-objective optimization techniques. International Journal of Advanced Manufacturing Technology, 24(9–10), pp. 675–685.

Umbers, I. & King, P. (1980). An analysis of human decision-making in cement kiln control and the implications for automation. International Journal of Man-Machine Studies, 12, pp. 11–23.

Venkataraman, R. (2000). Application of the methods of experimental design to quantify uncertainty in production profiles. In: Proceedings of Asia Pacific Conference on Integrated Modelling for Asset Management. 25–26 April 2000. Yokohama, Japan: Society of Petroleum Engineers. SPE 59422-MS.

Vilela, M., Oluyemi, G. & Petrovski, A. (2017). Value of information and risk preference in oil and gas exploration and production projects. In: Proceedings of Annual Caspian Technical Conference and Exhibition. 1–3 November 2017. Baku, Azerbaijan: Society of Petroleum Engineers. SPE 189044-MS.

Vilela, M., Oluyemi, G. & Petrovski, A. (2018). Fuzzy data analysis methodology for the assessment of value of information in the oil and gas industry. In: Proceedings of 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Rio de Janeiro, Brazil, July 9–13, 2018, pp. 1540–1546.

Vilela, M., Oluyemi, G. & Petrovski, A. (2019a). A fuzzy inference system applied to the value of information assessment for the oil and gas industry. Decision Making: Applications in Management and Engineering, 2(2), pp. 1‒18.

Vilela, M., Oluyemi, G. & Petrovski, A. (2019b). Fuzzy logic applied to value of information assessment in oil and gas projects. Petroleum Science, 16(5), pp. 1208–1220.

Wang, F. & White, Ch. (2002). Designed simulation for a detailed 3D turbidite reservoir model. In: Proceedings of Gas Technology Symposium. 30 April–2 May 2002. Calgary, Alberta, Canada: Society of Petroleum Engineers. SPE 75515-MS.

Warren, J. (1983). Development decision: Value of information. In: Proceedings of Hydrocarbon Economics and Evaluation Symposium of the Society of Petroleum Engineers of AIME. 3–4 March 1983. Dallas, Texas: Society of Petroleum Engineers. SPE 11312-MS.

White, Ch., Willis, B., Narayanan, K. & Dutton, Sh. (2001). Identifying and estimating significant geologic parameters with experimental design. In: Proceedings of Annual Technical Conference and Exhibition. 1–4 October 2001. Dallas, Texas, USA: Society of Petroleum Engineers. SPE 74140-PA.

Yang, C., Bi, X.Y. & Mao, Z.S. (2002). Effect of reaction engineering factors on biphasic hydroformylation of 1-dodecane catalyzed by water-soluble rhodium complex. Journal of Molecular Catalysis A: Chemical, 187(1), pp. 35–46.

Zadeh, L. (1965). Fuzzy sets. Information and Control, 8, pp. 338–353.

Zadeh, L. (1968). Probability measures of fuzzy events. Journal of Mathematical Analysis and Applications, 23, pp. 421–427.

Zadeh, L. (1971). Quantitative fuzzy semantics. Information Science Journal, 3(2), pp. 159–176.

Zang, T. & Green, L. (1999). Multidisciplinary design optimization techniques: Implications and opportunities for fluid dynamics research. In: Proceedings of 30th AIAA Fluid Dynamics Conference, Norfolk, Virginia, USA. June 28–July 1, pp. 1–20. Paper AIAA-99-3708.

Zimmermann, H. & Sebastian, H. (1994). Fuzzy design-integration of fuzzy theory with knowledge-based system-design. In: Proceedings of IEEE 3rd International Fuzzy Systems Conference, 1, pp. 352–357.

Zimmermann, H. (1996). Fuzzy logic on the frontiers of decision analysis and expert systems. In: Proceedings of the 1996 Biennial Conference of the North American Fuzzy Information Processing Society – NAFIPS, Berkeley, CA, USA, June 19–22, 1996, pp. 65–69.