Development of a Fuzzy System Model for Candidate-well Selection for Hydraulic Fracturing in a Carbonate Reservoir

Abstract

With current technology, it is only possible to extract 20% to 25% of the original oil in place from Iranian carbonate reservoirs, 10% less than the world average. In addition, formation damage is a serious problem in those reservoirs, which mainly caused by asphaltene precipitation, sand production, and ineffective stimulation method. The majority of mature carbonate reservoirs in Iran have low permeability and high skin values. Therefore, such reservoirs are capable of producing at commercial rates only if they are hydraulically fractured. Acid fracturing is usually reported as a standard method for fracturing in carbonate reservoirs. Hydraulic Fracturing (HF) technology, which was originally applied to overcome near wellbore damage, is a proper replacement stimulation method. It is evident that to adopt this technology, considerable efforts have to be strenuous in candidate-well selection. As asserted in the literature, even though a common practice, candidate-well selection is not a straightforward process and up to now, there has not been a well-defined approach to address this process. The techniques applied in HF candidate-well selection could be divided into two methods; conventional and advanced approaches. Conventional methods are not easy to use for nonlinear processes, such as candidate-well selection that goes through a group of parameters having different attributes and features such as geological aspect, reservoir and fluid characteristics, production details, etc. and that’s because it is difficult to describe properly all their nonlinearities. However, it is believed that advanced methods such as Fuzzy Logic (FL) could be better decrease the uncertainty existed in candidate-well selection. This paper presents a Mamdani fuzzy model where rules for HF candidate-well selection were derived from multiple knowledge sources such as existing literature, intuition of expert opinion to verify the gathered information. The needs for adapting HF as replacement stimulation in Iranina carbonate reservoirs are discussed and advanced methods for HF candidate selection will be reviewed in this paper. Also, the main reasons which show why propped HF is the choice in carbonate reservoirs will be discussed. Finally, the proposed Fuzzy system model is applied along with a case study in a carbonate reservoir.

Machine learning on field data for hydraulic fracturing design optimization

“…Particular case of stimulation in carbonate reservoirs is acid frac. Iranian field with 20 fractured well has been studied by [35] in order to test candidate selection procedure.…”

A review of fuzzy logic applications in petroleum exploration, production and distribution operations

“…The fuzzy inference system (FIS) logic is very helpful in regulating and managing incomplete information (Bello et al 2016). Hamdi-Cherif (2010) cited by Zoveidavianpoor et al (2012) listed merits and demerits of Fuzzy Logic to include the following: (a) Merits: FL mathematical models are veritable means for transforming human reasoning into useful set of rules that could easily be implemented using computer; fuzzy logic models are capable of handling any non-linear problems; FL models are vital techniques in addressing complex stochastic problems that cannot be solved by ordinary mathematical models; fuzzy logic models are robust and find application under information deficit scenarios; fuzzy models are adjudged as appropriate tools in generic decision-making.…”

A methodology for wellbore stability analysis of drilling into presalt formations: A case study from southern Iran

“…anhydrite and salt (Rolf et al, 2006;Gorjian et al, 2013). As a result, geomechanical characterization of the Gachsaran formations has been the subject of many studies in order to either evaluate their integrity during and after enhanced oil recovery (EOR) (Zoveidavianpoor et al, 2012) or finding solutions to prevent further issues during drilling or production stages (Dusseault et al, 2001;Rolf et al, 2006;Gorjian et al, 2013).…”