A Abbaszadeh shahri; R Hosseini; F Rezaei; K Mehdizadeh; N Panaei
Abstract
Artificial Neural Network methods (ANN) are computational methods, which capable to predict a specific log or classify different data. Unlike the digital computers, which require the completely definite and distinguished rules, the ANN methods do not need a pure mathematical model; rather like the human ...
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Artificial Neural Network methods (ANN) are computational methods, which capable to predict a specific log or classify different data. Unlike the digital computers, which require the completely definite and distinguished rules, the ANN methods do not need a pure mathematical model; rather like the human brain has the ability to learn by recognized and determined examples. The target of the present paper is to establish and prove the Petrophysical Analysis as powerful approach in prediction and diagnosis of rock reservoir porosity by use of petrophysical logs, in which by a high accuracy suggested Petrophysical Analysis based solution the porosity can be estimated using conventional logging data. On the basis of the available petrophysical data, the proposed method was examined in one of the southwest oil field of Iran. The obtained results of network analysis conditioning to reliability to data with different tests such as regression, root mean square and SPLine showed that the amount of network error in terms of available data in engineering range with a high acceptable safety factor could be used to predict and estimate porosity. This method with ability of cost reduction and viability can help and provide a large variety in this field for further extended research.
M. A. Oladzad Abbas Abadi; B. Movahed
Abstract
Hydrocarbon potential Evaluation of formations by using ∆logR method (a method based on separation of well logging of porosity DT/CN./RHOB) and resistivity well logging (Rt). This method has been today applied as an appropriate method in many of famous wells of the world. The beginning of these methods ...
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Hydrocarbon potential Evaluation of formations by using ∆logR method (a method based on separation of well logging of porosity DT/CN./RHOB) and resistivity well logging (Rt). This method has been today applied as an appropriate method in many of famous wells of the world. The beginning of these methods drew attention of many researchers in 1980. It had organic matters on the well logging based on the influences of layers containing organic matters. Passey et al. (1990) provided away for predicting of rich of organic material in source rock that have a high accuracy and potential for studying extensive rang of maturity condition. The basis of this method is overlapping porosity well logging (sonic, neutron, density) scaled on the resistivity well logging and determining the degree of separation between these two loges and calculation of total organic carbon TOC and S2. Using this method we can gain appropriate relative evaluation of formations without preparing sample during times of exploration. In this study, the areas which have rich organic matter of Gadvan formation in the SP-A well located in the South Pars Area have been deter mind with use of ∆logR way and for SP-A well, yielding results of this studies was compared with data relating to Rock – Eval Pyrolysis analysis of core samples and was observed good correlation.
M. Paryab
Abstract
Hydrocarbon source rocks, the subject of various analyses and investigations, are well appreciated because of their capacity for oil generation. In the present study, attempts were made to evaluate source rock using a leap cost analytical method. For this purpose, some samples from these formations were ...
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Hydrocarbon source rocks, the subject of various analyses and investigations, are well appreciated because of their capacity for oil generation. In the present study, attempts were made to evaluate source rock using a leap cost analytical method. For this purpose, some samples from these formations were analyzed by Rock- Eval. CO2 generated from these samples at 350ºС are calculated as mgHy/gr rock or Kg Hy/Ton rock. As this method requires more time and is relatively expensive, we offered a new method in which by calibration of data, obtained from analyzed samples, the result could be attributed to the whole interval of a formation. By calculation of S2, Tmax and TOC of analyzed samples collected from wells A and B for both Pabdeh and Gurpi formations in accordance with ∆LogR method, rescaling of Resistivity-sonic logs, Resistivity-Density logs and Resistivity-Neutron logs, TOC content of these formations were estimated. Comparison between these data and data obtained from direct sample measurements in lab and extrapolation of an equation that relates these data to S2 and TOC of sample analysis, TOC and S2 content of whole intervals of these formations were calculated through ∆LogR method. Then hydrocarbon generation potential of the Pabdeh and Gurpi formations were finally evaluated. These data were processed in a neural network method with forward back propagation capability designed as try and error structure within Matlab software. Final results are in good agreement with those data obtained from direct measurements.
