×

An automatic well planner for complex well trajectories. (English) Zbl 1477.86017

Summary: A data-driven automatic well planner procedure is implemented to develop complex well trajectories by efficiently adapting to near-well reservoir properties and geometry. The procedure draws inspiration from geosteering drilling operations, where modern logging-while-drilling tools enable the adjustment of well trajectories during drilling. Analogously, the proposed procedure develops well trajectories based on a selected geology-based fitness measure using an artificial neural network as the decision maker in a virtual sequential drilling process within a reservoir model. While neural networks have seen extensive use in other areas of reservoir management, to the best of our knowledge, this work is the first to apply neural networks on well trajectory design within reservoir models. Importantly, both the input data generation used to train the network and the actual trajectory design operations conducted by the trained network are efficient calculations, since these rely solely on geometric and initial properties of the reservoir, and thus do not require additional simulations. Therefore, the main advantage over traditional methods is the highly articulated well trajectories adapted to reservoir properties using a low-order well representation. Well trajectories generated in a realistic reservoir by the automatic well planner are qualitatively and quantitatively compared to trajectories generated by a differential evolution algorithm. Results show that the resulting trajectories improve productivity compared to straight line well trajectories, both for channelized and geometrically complex reservoirs. Moreover, the overall productivity with the resulting trajectories is comparable to well solutions obtained using differential evolution, but at a much lower computational cost.

MSC:

86A20 Potentials, prospecting
68T07 Artificial neural networks and deep learning
86A32 Geostatistics

Software:

DeMat; FieldOpt
PDF BibTeX XML Cite
Full Text: DOI

References:

[1] Alizadeh, B.; Najjari, S.; Kadkhodaie-Ilkhchi, A., Artificial neural network modeling and cluster analysis for organic facies and burial history estimation using well log data: A case study of the south pars gas field, Persian Gulf Iran, Computers Geosci, 45, 261-269 (2012)
[2] Arbus T, Wilson S (2019) Cybersteering: Automated geosteering by way of distributed computing and graph databases in the cloud. In: Unconventional resources technology conference (URTeC); Society of Petroleum Engineers, pp 1361-1368
[3] Baumann, EJM; Dale, SI; Bellout, MC, FieldOpt: A powerful and effective programming framework tailored for field development optimization, Computers Geosci (2020)
[4] Bellout, MC; Echeverría Ciaurri, D.; Durlofsky, LJ; Foss, B.; Kleppe, J., Joint optimization of oil well placement and controls, Comput Geosci, 16, 4, 1061-1079 (2012)
[5] Bouzarkouna, Z.; Ding, DY; Auger, A., Well placement optimization with the covariance matrix adaptation evolution strategy and meta-models, Comput Geosci (2011)
[6] Chakraborty, UK, Advances in differential evolution vol. 143 (2008), New York: Springer, New York
[7] Collobert, R.; Weston, J.; Bottou, L.; Karlen, M.; Kavukcuoglu, K.; Kuksa, P., Natural language processing (almost) from scratch, J Mach Learn Res, 12, 2493-2537 (2011) · Zbl 1280.68161
[8] Fonseca R, Della Rossa E, Emerick A, Hanea R, Jansen J (2018) Overview of the Olympus field development optimization challenge. In: 16th European Conference on the Mathematics of Oil Recovery, pp. 1-10. European Association of Geoscientists & Engineers. doi:10.3997/2214-4609.201802246 · Zbl 1452.00021
[9] Forouzanfar, F.; Reynolds, AC; Li, G., Optimization of the well locations and completions for vertical and horizontal wells using a derivative-free optimization algorithm, J Petrol Sci Eng, 86, 272-288 (2012)
[10] Haghshenas, Y.; Emami Niri, M.; Amini, S.; Amiri Kolajoobi, R., Developing grid-based smart proxy model to evaluate various water flooding injection scenarios, Petrol Sci Technol, 38, 17, 870-881 (2020)
[11] Hassani H, Sarkheil H, Foroud T, Karimpooli S, et al (2011) A proxy modeling approach to optimization horizontal well placement. In: 45th US Rock Mechanics/Geomechanics Symposium. American Rock Mechanics Association
[12] Jean S, Cho K, Memisevic R, Bengio Y (2014) On using very large target vocabulary for neural machine translation. arXiv preprint arXiv:1412.2007
[13] Kristoffersen B, Silva T, Bellout, MC, Berg, CF (2020) An automatic well planner for efficient well placement optimization under geological uncertainty. In: 17th european conference on the mathematics of oil recovery, pp. 1-16. European Association of Geoscientists & Engineers
[14] Lesso Jr W, Kashikar S, et al (1996) The principles and procedures of geosteering. In: International association of drilling contractors international/society of petroleum engineers drilling conference and exhibition. Society of Petroleum Engineers
[15] Li Q, Omeragic D, Chou L, Yang L, Duong K, et al (2005) New directional electromagnetic tool for proactive geosteering and accurate formation evaluation while drilling. In: SPWLA 46th annual logging symposium. Society of Petrophysicists and Well-Log Analysts
[16] Maus S, Gee T, Mitkus A.M, McCarthy K, Charney E, Ferro A, Liu Q, Lightfoot J, Reynerson P, Velozzi D.M, et al (2020) Automated geosteering with fault detection and multi-solution tracking. In: International Association of Drilling Contractors International/Society of Petroleum Engineers Drilling Conference and Exhibition. Society of Petroleum Engineers
[17] Mikolov T, Deoras A, Povey D, Burget L, Černockỳ J (2011) Strategies for training large scale neural network language models. In: 2011 IEEE Workshop on Automatic Speech Recognition & Understanding, pp. 196-201. IEEE
[18] Price, KV; Storn, RM; Lampinen, JA, Differential evolution: A practical approach to global optimization (2005), New York: Springer, New York
[19] Sayyafzadeh, M.; Alrashdi, Z., Well controls and placement optimisation using response-fed and judgement-aided parameterisation: Olympus optimisation challenge, Comput Geosci (2019) · Zbl 1452.86015
[20] Sen S, Ganguli S.S, et al (2019) Estimation of pore pressure and fracture gradient in Volve field, Norwegian North Sea. In: SPE Oil and Gas India Conference and Exhibition. Society of Petroleum Engineers
[21] Shahkarami A, Mohaghegh S.D, Gholami V, Haghighat S.A, et al (2014) Artificial intelligence (AI) assisted history matching. In: SPE western North American and Rocky Mountain joint meeting. Society of Petroleum Engineers
[22] Stanley K.O (2004) Efficient evolution of neural networks through complexification. Ph.D. thesis, Department of Computer Sciences, The University of Texas at Austin
[23] Stanley, KO; Miikkulainen, R., Evolving neural networks through augmenting topologies, Evolut Comput, 10, 2, 99-127 (2002)
[24] Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9
[25] Tompson J.J, Jain A, LeCun Y, Bregler C (2014) Joint training of a convolutional network and a graphical model for human pose estimation. In: Proceedings of the 27th international conference on neural information processing systems, vol 1. Advances in neural information processing systems, pp. 1799-1807
[26] Winkler, H., Geosteering by exact inference on a bayesian network, Geophysics, 82, 5, D279-D291 (2017)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.