Improved Reservoir Permeability Models From Flow Units And Soft Computing Techniques: A Case Study, Suria And Reforma-Libertad Fields, Colombia R. Soto B.; R. Soto B. ECOPETROL-ICP Search for other works by this author on: This Site Google Scholar F. Torres; F. Torres American Geoexploration Search for other works by this author on: This Site Google Scholar S. Arango; S. Arango American Geoexploration Search for other works by this author on: This Site Google Scholar G. Cobaleda G. Cobaleda ECOPETROL-ICP Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, Buenos Aires, Argentina, March 2001. Paper Number: SPE-69625-MS https://doi.org/10.2118/69625-MS Published: March 25 2001 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Soto B., R., Torres, F., Arango, S., and G. Cobaleda. "Improved Reservoir Permeability Models From Flow Units And Soft Computing Techniques: A Case Study, Suria And Reforma-Libertad Fields, Colombia." Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, Buenos Aires, Argentina, March 2001. doi: https://doi.org/10.2118/69625-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Latin America and Caribbean Petroleum Engineering Conference Search Advanced Search AbstractThis 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.IntroductionThe 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 Keywords: machine learning, interval well, fluid dynamics, permeability, flow unit, fzi value, multivariate statistical analysis, neural network, core permeability, fuzzy logic Subjects: Reservoir Fluid Dynamics, Information Management and Systems, Flow in porous media This content is only available via PDF. 2001. Society of Petroleum Engineers You can access this article if you purchase or spend a download.