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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractIn the last years, the concept of hydraulic flow units (HFU) has been used in the petroleum industry to improve prediction of permeability in uncored interval/wells. This concept is strongly related to the flow zone indicator (FZI) which is a function of the reservoir quality index (RQI). Both measures are based on porosity and permeability of cores. It is assumed that samples with similar FZI values belong to the same HFU. Thus the FZI, along with other significant variables such as porosity, can be used directly to estimate the permeability in a zone; however, the FZI has to be estimated first. In this paper, a novel method based on hybrid soft computing techniques is used for permeability predictions. The technique is known as adaptive network-based fuzzy inference systems or ANFIS, and is based on adaptive neural networks and fuzzy inference systems (FIS). The final inference on FZI is made by a FIS but the parameters of this FIS will be estimated through a learning procedure based on input data; such procedure is typically used in neural network training. The technique is applied in 3 steps: (1) Identification of the dominant variables in rock type behavior, (2) Development of an ANFIS which best suits the real model, using the dominant variables as input and the FZI as output.(3) Estimation of permeability from FZI and porosity values.These steps are applied on a sample case and the results show that hybrid soft computing techniques offer powerful tools for further improving permeability predictions.

This paper presents a methodology that looks to solve the inverse problem of predicting reservoir properties on uncored intervals/wells, using soft computing techniques (neural networks and fuzzy logic), multivariate statistical analysis and hydraulic flow unit concepts. Our methodology to improve the prediction of permeability in Suria and Reforma-Libertad fields in Colombia is the following:Data quality control. We apply multivariate statistical analysis for quality control of core and log data: 95% confidence ellipses and Q-Q plots are used for that purpose.Rock type identification. We use poral geometry analysis to identify rock types in cored wells. Then, fuzzy logic, core and log variables are used to develop a rock type model to be used in solving the inverse problem, predict the rock type in uncored intervals/wells.Hydraulic flow unit classification. For that purpose, we use the technique based on a modified Kozeny-Carmen equation to calculate the reservoir quality index, RQI=0.0314(K/f)½, flow zone indicator, FZI=RQI/(f/(1-f)) and fz=f/(1-f). The basic idea of hydraulic flow unit (HFU) classification is to identify classes that form unit-slope straight lines on a log-log plot of RQI vs. fz with similar but not identical FZI value. Each class or hydraulic flow unit has a mean FZI value at the intercept with fz=1, and a maximum and a minimum FZI values. We use log data and the fuzzy logic rock type variable to develop a neural network FZI model to be used in solving the inverse problem, predict FZI in uncored intervals/wells. The HFU for each uncored interval is determined with its FZI value that falls on a range between maximum and minimum values of FZI. Finally, permeability is calculated knowing its porosity and FZI values. In the literature, the HFU is first determined by Bayesian inference assigning a probability distribution of log values to each HFU and identifying to which population the given set of log readings most likely belong. Then, permeability is estimated from porosity and mean FZI values ignoring the scatter data for each HFU. Permeability estimations from our approach are compared from other conventional techniques to demonstrate that this is a better way to get confident permeability models and to show how to used soft computing techniques to improve reservoir description. Introduction The degree of success of many oil and gas drilling, completion, and production activities depends on the accuracy of the models used in a reservoir description. Permeability is an important parameter in a heterogeneous reservoir characterization. Formation permeability controls the strategies involving well completion and stimulation, and reservoir management. For a low-permeability reservoir, a hydraulic fracture treatment may be needed to optimize the oil and gas recovery. In other zones, a matrix acid treatment might be more economic. The optimal well-spacing and well-production rates are dependent of the formation's permeability values. In a high-permeability formation, we may drill fewer wells to drain the reservoir. Therefore, permeability is a key parameter in any reservoir characterization that governs in great extension its handling and development. Permeability is usually measured in laboratory on core samples. However, most drilled wells are not cored. As a result, models are needed to estimate permeability in uncored but logged wells. This is known as the inverse problem.1,2

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