An enhanced ANFIS based approach for estimating petrophysical properties of thinly laminated reservoirs in absence of core data

Obtaining reliable petrophysical estimation of the thinly laminated reservoirs is a major challenge due to the convoluting effect of laminations on the conventional logging tools that jeopardizes the accuracy of the interpretations and hence poses major risks to the resource evaluation and field development planning. Many existing parametric or non-parametric correlations fail to provide reliable estimations and therefore domain expert knowledge is critical in the studies pertaining to thinly laminated reservoirs. We present a two-step approach based on neuro-fuzzy inference system (FIS), and harnessing the available expert knowledge for proper depositional environment (DE) analogues that determine the ranges of the parameters for the estimation exercise. Application of the method to the data from different DEs shows satisfying outcome. The use of a FIS to mine for proper DE that represents the thinly laminated sand reservoir at hand, prior to use of ANFIS, sets the proposed approach apart from other available research based on conventional applications of non-parametric regression methods. The proposed approach provides a pragmatic tool for estimating the properties of thinly laminated sands when no core data is available. It, therefore, greatly improves the modeling of laminated reservoirs, and therefore helps mitigate the risks associated with the field development.