LITHOLOGY INTERPRETATION BASED ON WELL LOG DATA ANALYSIS IN “JS” FIELD
نویسندگان
چکیده
منابع مشابه
Separating Well Log Data to Train Support Vector Machines for Lithology Prediction in a Heterogeneous Carbonate Reservoir
The prediction of lithology is necessary in all areas of petroleum engineering. This means that to design a project in any branch of petroleum engineering, the lithology must be well known. Support vector machines (SVM’s) use an analytical approach to classification based on statistical learning theory, the principles of structural risk minimization, and empirical risk minimization. In this res...
متن کاملUnderground contour (UGC) mapping using potential field, well log and comparing with seismic interpretation in Lavarestan area
Coastal Fars gravimetry project in Fars province was carried out to find the buried salt domes and to determine characteristics of faults in this area. The Lavarestan structure was covered by 4203 gravimetry stations in a regular grid of 1000*250 m. Depth structural model of this anticline made in previous studies was based on geological evidences and structural geology measurements. In order t...
متن کاملzonation of two oil reservoirs in iran with a statistical method based on well log data
different methods exist for the zonation of oil reservoirs based on petrophysical data and well logs. among them are: permeability-porosity cross polt, pickett and soder and gill methods. in this study a statistical zonation technique has been used for the marun and pazanan oil reservoirs in iran based on effective porosity, density, sonic, neutron and resistivity data. petrophysical interpreta...
متن کاملseparating well log data to train support vector machines for lithology prediction in a heterogeneous carbonate reservoir
the prediction of lithology is necessary in all areas of petroleum engineering. this means that todesign a project in any branch of petroleum engineering, the lithology must be well known. supportvector machines (svm’s) use an analytical approach to classification based on statistical learningtheory, the principles of structural risk minimization, and empirical risk minimization. in thisresearc...
متن کاملApplication of artificial neural networks for the prediction of carbonate lithofacies, based on well log data, Sarvak Formation, Marun oil field, SW Iran
Lithofacies identification can provide qualitative information about rocks. It can also explain rock textures which are importantcomponents for hydrocarbon reservoir description Sarvak Formation is an important reservoir which is being studied in the Marun oilfield, in the Dezful embayment (Zagros basin). This study establishes quantitative relationships between digital well logs data androutin...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Applied Research on Civil Engineering and Environment (ARCEE)
سال: 2019
ISSN: 2714-6553
DOI: 10.32722/arcee.v1i01.1955