Permeability Prediction Using Hydraulic Flow Units and Hybrid Soft Computing Systems

Abstract

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.

Pore-Type Determination From Core Data Using a New Polar-Transformation Function from Hydraulic Flow Units

“…According to Tiab et al (1993), for a naturally fractured or vuggy reservoir system, permeability is given as a function of total porosity, specific surface area, and m as follows: Therefore, following the hydraulic flow units concept, Ohen et al (2001) And using the same concept of hydraulic units, HU, for a matrix pore system, a plot of FRQI versus φ z on a log-log scale will delineate the flow units (Soto B. et al 1993(Soto B. et al , 2001. …”

Hydraulic-Unit-Based Fuzzy Model to Predict Permeability from Well Logs and Core Data of a Multi-Layer Sandstone Reservoir in Ulleung Basin, South Korea

“…They used two different sample cases: a channel-type carbonate reservoir and a sandstone reservoir. Soto et al (2001) developed a fuzzy model that predicted permeability using FZI and HU grouping, and showed its improved prediction accuracy. Shahvar et al (2009) Xiao et al (2013) estimated permeability from NMR logs by applying HU grouping using FZI.…”

Production-System Optimization of Gas Fields Using Hybrid Fuzzy/Genetic Approach

“…Recently, fuzzy-logic systems have been used to solve a number of different problems in a variety of areas, including physical and chemical systems, production planning and scheduling, location and transportation problems, resource allocation in financial systems, and engineering design. In the petroleum industry, fuzzy logic has been put to work in many different areas since the early 1990s, including reservoir characterization (Hambalek and Gonzalez 2003;Lim and Kim 2004;Rafiei et al 2009;Shokir 2006;Soto et al 2001;Taghavi 2005;Zhou et al 1993;Cuddy 2000;Kedzierski and Mallet 2006;Finol et al 2002;Hajizadeh 2007a), optimal well operations (Alimonti and Falcone 2004; Lababidi 2003, 2005;Rivera 1994;Dumans 1995;Xiong et al 2001), stimulation treatment (Xiong and Holditch 1995;Nitters et al 2000), and economic analysis (Zolotukhin 2000;de Salvo Castro and Fereira Filho 2001;Agbon and Araque 2003;Chang et al 2006). An extensive list of such applications is presented in Table 1.…”