Optimal developmental and complementary well placement in one of the southwest Iranian oil fields using geostatistical methods and applying objective function


Department of Mining and Metallurgical Engineering, Yazd University, Yazd, Iran


Considering the necessity of recognizing subsurface targets and productive zones and the high cost of drilling operations, especially exploratory drilling and the high risk of exploration for hydrocarbon reservoirs, determining the optimal drilling location of oil and gas wells is important that can have a significant impact on the economics of oil projects. The purpose of this study was to optimally design a suitable site for drilling oil and gas wells in one of the southwestern Iranian hydrocarbon reservoirs using geostatistical methods for detailed exploration and increasing of efficiency. For this purpose, firstly, geostatistical estimation of kriging was used to estimate the petrophysical parameters of this reservoir and the porosity, water saturation and kriging variance of these parameters were calculated in the designed block model. By defining an objective function, modifications of this function were considered to result in maximization of porosity, hydrocarbon saturation and their kriging variance in the blocks located in a vertical direction. Therefore, the optimum drilling point is suggested in the coordinate which maximizes the objective function in this orthogonal direction. For this purpose, considering two different approaches, 5 optimal drilling locations for developmental wells and 5 optimal drilling locations for complementary wells were proposed in order of drilling priority. Estimates have shown that drilling of developmental wells can increase in-situ hydrocarbon reserve by 26.3% and drilling of complementary wells can reduce the variance and estimation error of porosity by 10% and 10.4%, respectively and those of water saturation by 8.3% and 2.9%, respectively.


Aasum, Y., Kelkar M.G., Gupta S.P., 1991. An application of geostatistics and fractal geometry for reservoir characterization. SPE Formation Evaluation 6(1), 9-11.
Aghajari, A., Sasaninia, N., 2017. Estimation of Porosity and Permeability by Using Geostatistical Methods in one of the OilFields SW of Iran. Revista Publicando 13, 236-248.
Bourgoyne, A.T., Millheim, K.K., Chenevert, M.E., Young, F.S., 1986. Applied Drilling Engineering, p. 502.
Chen, H., Feng, Q., Zhang, X., Wang, S., Zhou, W., Geng, Y., 2017. Well placement optimization using an analytical formula-based objective function and cat swarm optimization algorithm. Petroleum Science and Engineering 157, 1067-1083.
Deusth, C.V., Journel, A.G., 1992. GSLIB: Geostatistical software Library and users guide. Oxford University Press, New York. p. 340.
Deutsch, C.U., 2002. Geostatitistical Reservoir Modeling, Oxford University Press, Oxford.
Hamida, Z., Azizi, F., Saad, G., 2017. An efficient geometry-based optimization approach for well placement in oil fields. Petroleum Science and Engineering 149, 383-392.
Hasanipak, A.A., 2010. Geostatistics. Third edition, Tehran University Press. (In Persian).
Madani, H., 1994. Basics of Geostatistics, Amir Kabir University of Technology Publications. (In Persian).
Mohammadi, H., Seifi, A., Foroud, T., 2012. A robust Kriging model for predicting accumulative outflow from a mature reservoir considering a new horizontal well. Petroleum Science and Engineering 82, 113-119.
Mohseni, H., Rafiee, B., Behzad, R., Zahrab, Zadeh M., 2010. 3D Modeling of Carbonate Reservoirs Using Geostatistics, Stratigraphy and Sedimentology Research. (In Persian).
Nwachukwu, A., Jeong, H., Pyrcz, M., W.Lake, L., 2018. Fast evaluation of well placements in heterogeneous reservoir models using machine learning. Petroleum Science and Engineering 163, 463-475.
Rahim, Sh., Li, Z., 2015. Well Placement Optimization with Geological Uncertainty Reduction. IFAC-Papers on Line 48, 57-62.
Yeten, B., Gümrah, F., 2000. The use of fractal geostatistics and artificial neural networks for carbonate reservoir characterization. Transport in Porous Media 41(2), 173-953.