Forecasting oil production for Oligocene C sequence, X field, Cuu Long basin using logistic growth model
Abstract
Hydrocarbon production forecasting for the field lifetime in the short and long term is an important phase, the accuracy of this process plays a tremendous role in giving the decision of reasonable field management and development. In this article, the logistic growth models using the function MATLAB’s ‘nlinfit’ have been built to forecast oil production yield for the Oligocene C sequence, X field, Cuu Long basin. Thanks to the combination with the history matching process, the logistic growth model expressed high accuracy, the results of the model are very close to the actual production data with a relative error of 1,85%. The article analyzed and evaluated the production parameters of wells obtained when building logistic growth models such as the time at which half of the carrying capacity has been produced, the steepness of the decline of the rate, and the production rate of the wells at the forecast time. Without applying any improved oil recovery method, the decline of the rate of all wells approaches 100 bbl/d before reaching the validity period of the oil and gas contract. This is the basis for operators to establish and improve field development plans.References
Arps J.J., (1945) Analysis of Decline Curves. AIME, 160, 228-247.
Campbell C. J., (1997). The coming oil crisis. Brentwood, Essex, UK: Multi-Science Publishing; and Dublin, Republic of Ireland: Petroconsultants.
Campbell C.J,, Heapes, S. (2008). An atlas of oil and gas depletion. Huddersfield, UK: Jeremy Mills Publishing.
Clark A.J., Lake L.W., Patzek, T.W., (2011). Production forcasting with Logistic Growth Models. SPE Annual Technical Conference and Exhibition, Denver, Colorado, USA, 30 October - 2 November.
Juvkam-Wold H. C., Dessler, A. J., (2009). "Using the Hubbert equation to estimate oil reserves." World Oil 230(4).
Hubbert M. K., (1956). Nuclear energy and the fossil fuels. In Meeting of the Southern District, Division of Production, American Petroleum Institute, San Antonio, TX, USA, 7–9 March 1956. San Antonio, TX: Shell Development Company.
Hubbert M. K., (1982). Techniques of prediction as applied to the production of oil and gas. In Proc. Symp. on Oil and Gas Supply Modeling, Washington, DC, USA, 18–20 June 1980. NBS Spec. Publ. no. 631, pp. 16–141. Washington, DC: US Department of Commerce.
Laherrefre J., (2000). Learn strengths, weaknesses to understand Hubbert curve. Oil Gas J. 98(16), 63–76.
Laherrefre J., (2002). Modelling future liquids production from extrapolation of the past and from ultimates. Energy exploration & exploitation 20 (6), 457–479.
Laherrefre J., (2004). Oil and natural gas resource assessment: production growth cycle models. In Encyclopaedia of energy (ed. CJ Cleveland). Elsevier. Amsterdam, 617–631.
Mohr S., Evans, G., (2008). Peak oil: testing Hubbert’s curve via theoretical modeling. Natural Resources Research 17(1), 1–11.
Nashawi I. S., Malallah, A., Al-Bisharah, M., (2010). Forecasting world crude oil production using multi-cyclic Hubbert model. Energy Fuels 24, 1788–1800.
Nguyễn Văn Hùng và Lê Phúc Nguyên, (2019). Phát triển mô hình dự báo khai thác cho các giếng dầu khí. Tạp chí dầu khí 8, 14 – 20.
Sorrell S., Speirs, J., (2014). Using growth curves to forecast regional resource recovery: approaches, analytics and consistency tests. Phil. Trans. R. Soc. A 372: 20120317.
Tsoularis A. and Wallace, J., (2002) "Analysis of logistic growth models." Mathematical Biosciences 179(1), 21-55.
Trần Đăng Tú, Đinh Đức Huy, Trần Xuân Quý, Phạm Trường Giang, Lê Vũ Quân, Lê Thế Hùng, Lê Quốc Trung, Trần Nguyên Long, (2019). Nghiên cứu ứng dụng mô hình tăng trưởng logistic để dự báo khai thác cho tầng Miocene dưới, mỏ Bạch Hổ. Tạp chí dầu khí 9, 16 – 22.
Verhulst P. F., (1838), Notice sur la loi que la population suit dans son accroissement, Correspondance mathématique et physique 10, 113-121.
