COMPLEX LITHOLOGY ANALYSIS [FUZZY APPROACH]

COMPLEX LITHOLOGY ANALYSIS[FUZZY APPROACH]Presented by

OBOT, ITORO A.G2011/MENG/PNG/FT/851Department of Petroleum & Natural Gas EngineeringUniversity of Port Harcourt

COURSE LECTURER: PROF. DULU APPAHCOURSE CODE/TITLE: PNG 613.2/ FORMATION EVALUATION

JULY 2014

OUTLINEINTRODUCTIONLITHOLOGY ANALYSIS TOOLS

WELL LOG ANALYSIS PROCEDUREFUZZY METHODCASE STUDYCONCLUSIONINTRODUCTIONPhysical Characteristics of a rockAnalysis of drill core and Geophysical loggingImproved in-situ determination of rock characteristics and properties.gamma ray logs, neutron logs, density logs, deep resistivity logs, and shallow resistivity logs were identified as lithology logscombination of these different borehole logging techniques would provide information related to rock characteristics and propertiesFuzzy Method of Well Log AnalysisLITHOLOGY ANALYSIS TOOLSGAMMA RAY LOGS

Gamma ray logs measure natural radioactivity in formations. Shale-free sandstones and carbonates give low gamma ray readings. As shale content increases, the gamma ray log response also increases.[units and representation]LITHOLOGY ANALYSIS TOOLSTHE RESISTIVITY LOG

The resistivity log is a measure of a formations resistivity. Most rock materials are essentially insulators, while their enclosed fluids are conductors. When a formation is porous and contains salty water, the resistivity will be lowLITHOLOGY ANALYSIS TOOLSTHE RESISTIVITY LOGDeep resistivity is the resistivity recorded farther away from the inversion core created by the drilling mud. Shallow resistivity log is the resistivity recorded close to the oil well bore.Shales show low resistivity values with high gamma ray values.LITHOLOGY ANALYSIS TOOLSLITHOLOGY ANALYSIS TOOLSTHE DENSITY LOG continuous record of a formations bulk density.determination of porosity, hydrocarbon density, oil-shale yield, the differentiation between liquids and gases (when used in combination with neutron log), evaluation of shaly sands and complex lithologies, calculation of overburden pressure and rock mechanical properties.Variation of density indicates porosity changes.organic content = low density, low density = high porosity and vice-versa.LITHOLOGY ANALYSIS TOOLSTHE NEUTRON LOG lithology identification, delineation of porous formations and determination of porosity, and the differentiation between liquids and gases when used in combination with density log.On crossplot of neutron and density logs, pure shale can be recognized by the high neutron value relative to the density value which gives a large positive separation to the logs while gas stands out distinctly giving a large negative separation.They respond primarily to the amount of hydrogen in the formation.LITHOLOGY ANALYSIS TOOLS

WELL LOG ANALYSIS PROCEDURE: FUZZY METHODA Fuzzy Inference System (FIS) is a system that uses fuzzy set theory to map inputs (features in the case of fuzzy classification) to outputs (classes in the case of fuzzy classification)Fuzzy rules are a collection of linguistic statements that describe how the FIS should make a decision regarding classifying an input or controlling an output. Fuzzy rules are always written in the following form:if (input1 is membership function1) and/or (input2 is membership function2) and/or (?) then (outputn is output membership functionn).WELL LOG ANALYSIS PROCEDURE: FUZZY METHODA. DATA IDENTIFICATIONB. DATA SANITIZATIONC.CORRELATION TESTD.NORMALIZATIONE. GENERATING CLUSTERSF. FUZZIFICATION

WELL LOG ANALYSIS PROCEDURE: FUZZY METHODA. DATA IDENTIFICATIONMainly from open-hole wireline sub-surface logging data report.WELL LOG ANALYSIS PROCEDURE: FUZZY METHODB. DATA SANITIZATIONThe raw well logs were inspected and sanitized to eliminate negative and null values were removed from the log data. This includes readings gotten when the tool is not moving or unrelated spike that might have been recorded.WELL LOG ANALYSIS PROCEDURE: FUZZY METHODC.CORRELATION TESTThe raw well logs are subjected to a statistical correlation test, to determine the relationships among the data elements for the purpose of clustering them. The correlation test was carried out on the log values to determine if there was any relationship between the log data values.

WELL LOG ANALYSIS PROCEDURE: FUZZY METHODD.NORMALIZATIONThe raw well log values were normalized (in the range 0 to 1) for the purpose of rendering the data dimensionless, and removing the effect of scaling on the values.WELL LOG ANALYSIS PROCEDURE: FUZZY METHODE. GENERATING CLUSTERS

Self Organizing Map (SOM) of neural networks for the determination of oil well lithology and fluid contents is used here. It consist of tools for building human-like intelligence system.The normalized log values were subjected to the clustering algorithm of the SOM neural network for the purpose of generating meaningful clusters so that the data sample can be assigned into their respective cluster group.The mean of the log values and their standard deviation are then computed. The computed mean of the log values are then used to infer the lithology and fluid content of the rock species that characterize the geological formation of the oil well being investigated by determining their fuzzy valueAfter training the SOM, the neural network would have learned the structure of the input data. The test data file is submitted to the trained SOM network, which then identifies the clusters it had recognized during the training process and the data samples are assigned to cluster groups. An output report typical of the form presented in Table 2 is generated.The mean of the log values and their standard deviation are then computed. The computed mean of the log values are then used to infer the lithology and fluid content of the rock species that characterize the geological formation of the oil well being investigated by determining their fuzzy value.17WELL LOG ANALYSIS PROCEDURE: FUZZY METHODF. FUZZIFICATIONThe mean and standard deviation values of the log data by clusters were computed to determine their fuzzy value which can be considered High, Moderate, or Low. These are important variables desirable for identifying the physical property of each cluster and subsequently generating a chart which shows the distribution of the rocks in the well and location of hydrocarbon and fluid content.

WELL LOG ANALYSIS PROCEDURE: FUZZY METHODF. FUZZIFICATION

The gamma ray log, which is the primary lithology log, was used to determine the primary lithology of the rock type. The resistivity logs were inspected to determine if there is any hydrocarbon presence indicated by an inversion of the deep resistivity logs and the shallow resistivity logs. The density and neutron logs are used to confirm either the presence of oil, gas or water in the rock materials matrix.19CASE STUDYBased on a study carried out by Akinyokun in 2002 on geophysical well log data from the Niger-Delta region of Nigeria. Twelve cluster groups were identified in the well log data.

The cluster groups (denoted by their cluster numbers), their mean values and computed standard deviations are presented in TableCASE STUDY

CASE STUDYIdentified Clusters.

CASE STUDY

Well Stratigraphy ChartCONCLUSION The fuzzy inference methodology adopted in the interpretation of the clusters was derived from the methods used in the interpretation of traditional graphical cross-plots by log analystsThe result not only gives the oil well lithology, it also gives an indication of the fluid content of the oil well and rock materials identified. The SOM based clustering and the fuzzy inference rules developed in this paper can form the basis for the development of a neuro-fuzzy expert system that can be used for the detection of fluid content in oil wells.THANKYOU