How to Improve Reservoir Characterization Models Using Intelligent Systems
The degree of success of many oil and gas drilling, completion, and production activities depends upon the accuracy of the models used in a reservoir description. Soft Computing techniques (neural networks, fuzzy logic, genetic algorithms) have demonstrated that it is possible to get more confident models compared with conventional techniques. However, before developing a soft-computing model, engineers must solve the problem of dimensionality such as determining dominant and irrelevant variables. Principal component and factor analyses can be applied to reduce the dimensionality and help soft computing techniques formulate more realistic models.
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) and multivariate statistical analysis. Concepts of multivariate statistical analysis, neural network, and fuzzy logic are integrated to get better petrophysical models. The first step in this methodology is to preprocess data, for which Q-Q plots are used with 95% confidence ellipses for quality control. After that, the dominant and the optimum number of independent variables are identified from core and well log using principal components, factor analysis and fuzzy logic concepts. Then, neural networks are applied to model any target variables. To check model validity, residual plots, average absolute errors, and correlation coefficients are used.
An intelligent Software have been used, Oilfield Intelligence (OI), as an engineering tool in this methodology. OI integrates neural networks, fuzzy logic and multivariate statistical analysis. It is composed of five main subsystems: database, preprocessing, architecture design, graphics design, and inference engine modules. OI was validated by obtaining confident models in three different oil field problems: (1) a neural network permeability model using porosity and gamma ray and a neural network pseudo-gamma ray log model using 3D seismic attributes for the reservoir VLE 196 Lamar field located in Block V of south-central Lake Maracaibo (Venezuela), (2) neural network primary ultimate oil recovery (PRUR), initial waterflooding ultimate oil recovery (IWUR), and infill drilling ultimate oil recovery (IDUR) models using reservoir parameters for San Andres and Clearfork carbonate formations in west Texas, and (3) neural network permeability model for K1 unit represented by 33 progressive transgressional subunits and deposits of sand and clay of marine origin form of Suria and Reforma-Libertad fields in Colombia.
KeywordsFuzzy Logic Neural Network Model Multivariate Statistical Analysis Residual Plot Average Absolute Error
Unable to display preview. Download preview PDF.
- 1.Soto B., R.: Reservoir Characterization Using Core, Well Log, and Seismic Data and Intelligent Software, Dissertation, Texas A&M University, College Station, Texas (1998).Google Scholar
- 2.Amaefule Jude and Altunbay Mehmet: Enhanced Reservoir Description: Using Core and Log Data to identify Hydraulic Flow Units and Predict Permeability in Uncored Intervals/Wells. paper SPE 26436 presented at the 68* Annual Technical Conference and Exhibition. 3–6 October 1993.Google Scholar
- 3.Maghsood Abbaszadeh, Hikari Fujii, and Fujio Fujimoto. Permeability Prediction by hydraulic Flow Units- Theory and Applications. Paper SPE 30158 presented at the PetroVietnam Conference 1–3 Maren 1996.Google Scholar
- 4.Timur, A.: “An Investigation of Permeability, Porosity, and Residual Water Saturation Relationships for Sandstone Reservoirs”, paper presented at the 1968 SPWLA Annual Logging Symposium, New Orleans, 23–26 June.Google Scholar
- 5.Berg, R.R.: “Method for Determining Permeability from Reservoir Rock Properties,” Trans. GCAGS (1970) 20,303.Google Scholar
- 6.Yao, C.Y. and Holditch, S.A.: “Estimating Permeability Profiles Using Core and Log Data,” paper SPE 26921 presented at the 1993 Eastern Regional Conference and Exhibition, Pittsburgh, 2–4 November.Google Scholar
- 7.Amaefule, J.O., Altunbay, M., Tiab, D., Kersey, D.G., and Keelan, D.K.: “Enhanced Reservoir Description: Using Core and Log Data to Identify Hydraulic(Flow) Units and Predict Permeability in Uncored Intervals/Wells,” paper SPE 26436 presented at the 1993 Annual Technical Conference and Exhibition, Houston, 3–6 October.Google Scholar
- 8.Soto B., R., Wu, C.H., and Bubela, A. M.: “Infill Drilling Recovery Models for Carbonate Reservoirs - A Multiple Statistical, Non-Parametric, and Neural Network Approach,” paper SPE 57458 presented at the 1999 SPE Eastern Regional Conference and Exhibition, Charleston, WV, October 21–22.Google Scholar
- 9.Soto B., R., and Holditch, S.A..: “Development of Reservoir Characterization Models Using Core, Well Log, and 3D Seismic Data and Intelligent Software,” paper SPE 57457 presented at the 1999 SPE Eastern Regional Conference and Exhibition, Charleston, WV, October 21–22.Google Scholar
- 10.Nikravesh, M., Kovscek, A.R., Johnston, R.M., and Patsek, T.W.: “Prediction of Formation Damage During Fluid Injection Into Fractured, Low Permeability Reservoirs via Neural Networks,” paper SPE 31103 presented at the 1996 SPE Formation Damage Symposium, Lafayette, LA, 16–18 February.Google Scholar
- 11.Garg, A., Kovscek, A.R., Nikravesh, M., Castanier, L.M., and Patzek, T.W.: “CT Scan and Neural Network Technology for Construction of Detailed Distribution of Residual Oil During Waterflooding,” paper SPE 35737 presented at the 1996 SPE Western Regional Meeting, Anchorage, AK, 22–24 May.Google Scholar
- 12.Juniardi, LR. and Ershaghi, I.: “Complexities of Using Neural Network in Well Test Analysis of Faulted Reservoirs,” paper SPE 26106 presented at the 1996 SPE Western Regional Meeting, Anchorage, AK, 22–24 May.Google Scholar
- 13.Zhou, CD., Wu, X.L., and Cheng, J.A.: “Determining Reservoir Properties in Reservoir Studies Using a Fuzzy Neural Network,” paper SPE 26430 presented at the 1993 SPE Annual Tech. Conference and Exhibition, Houston, TX, 8–12 October.Google Scholar
- 14.Johnson, R.A., and Wichern, D.W.: Applied Multivariate Statistical Analysis, Fourth Edition, Printice-Hall, Inc., Upper Saddle River, NJ (1998).Google Scholar
- 15.Jong-Se Lim, Joe M. Kang, and Jungwham Kim,“Multivariate Statistical Analysis for Automatic Electrofacies determination from well log measurements”, paper SPE 38028, presented at the 1997 SPE Asia Pacific Oil and Gas Conference.Google Scholar
- 16.Brown Malcom: “Fuzziness in the Field”. Review Oct-Dec 1998.Google Scholar
- 17.Fuzzy Logic Tollbox. “User’s Guide. Version 2 MATLAB”. Sept 2000.Google Scholar
- 18.Xiong Xongjie and Holditch S.A. “An Investigation Into the Application of Fuzzy Logic to Well Stimulation Treatmnt Design.” Paper SPE 27672 present at the Permian Basin Oil and Gas Recovery Conference. Maren 16–18,1994.Google Scholar