State-Of-The-Art in Permeability Determination From Well Log Data: Part 1- A Comparative Study, Model Development

Balan, B.; Mohaghegh, Shahab D.; Ameri, S.

doi:10.2118/30978-ms

Cited by 106 publications

(53 citation statements)

References 9 publications

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“…A Basement High trends found parallel to River Brahmaputra where thirteen wells have been drilled by national oil companies. Sedimentary sequences ranging in age from Late Mesozoic to Cenozoic are exposed in the Assam-Arakan basin (Balan et al 1995;Mandal and Dasgupta 2013). The study area, Chandmari oil field is located in the crested part of Basement high and bounded by two NE-SW-trending major faults in the northern and southern sides of the structure (Sekhar Deb and Barua 2010).…”

Section: Geological Settingmentioning

confidence: 99%

“…The most productive reservoirs in the upper basin are the Barail (Oligocene-Miocene) sands and the Tipam group (Miocene) massive sandstones. Other formations are Girujan (Miocene), Namsang (Pliocene) and Siwalik/Dhekiajuli (Recent) (Balan et al 1995;Mandal and Dasgupta 2013). Langpar formation (Paleocene) is followed by the basement at Pre-Cambrian age.…”

Section: Geological Settingmentioning

confidence: 99%

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Permeability prediction from wireline logging and core data: a case study from Assam-Arakan basin

Singh

2018

J Petrol Explor Prod Technol

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Permeability is an important petrophysical parameter of hydrocarbon reservoirs for oil and gas production. Formation permeability is often measured in the laboratory test using core samples. However, when few core samples are available to calculate the permeability in the field, estimation of permeability becomes a challenging task. In study area, the Chandmari field of upper Assam-Arakan basin with the availability of only seven core samples and conventional logs such as density, porosity, resistivity and gamma ray data from few wells, the estimation of permeability becomes a difficult task. Therefore, in the present study an attempt is made to estimate the permeability from well log and core data using Buckles' method approach in Langpar and Lakadong + Therria sanstone reservoir of Eocene-Paleocene geologic age in the field under the assumption and geological support that reservoirs in the study area are clean sand having very less shale control and are homogenous reservoir with little/no heterogeneity. In this study, petrophysical evaluation from log data and that from core data are integrated for the analysis of the reservoir characteristics. The relationship between porosity and water saturation which is required to distinguish mobile from capillary bound water or irreducible water saturation is used to estimate the irreducible water saturation. The estimated irreducible water saturation which is an essential parameter for water cut and permeability estimation is used for estimating the permeability in the field. The estimated permeability in the reservoirs using Buckles' method ranging from 1500 to 4554.38 mD is well matched with the permeability estimated from core sample. The estimated permeability results suggest that the oil reservoir has the higher permeability than the gas reservoir. The permeability estimation relationship can further be used for the estimation of permeability in the inter-well region of Chandmari oil field.

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Section: Geological Settingmentioning

confidence: 99%

Section: Geological Settingmentioning

confidence: 99%

Permeability prediction from wireline logging and core data: a case study from Assam-Arakan basin

Singh

2018

J Petrol Explor Prod Technol

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“…Artificial neural networks are a set of a relatively large number of processing elements (artificial neuron) that are designed in a specific and regular manner and the signals exchange between neurons on the communicational links [10]. Artificial neurons are similar to biological neurons of the human brain.…”

Section: Design and Development Of The Neural Networkmentioning

confidence: 99%

Prediction of Shear Wave Velocity Using Artificial Neural Network Technique, Multiple Regression and Petrophysical Data: A Case Study in Asmari Reservoir (SW Iran)

Akhundi¹,

Ghafoori²,

Lashkaripour³

2014

OJG

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Shear wave velocity has numerous applications in geomechanical, petrophysical and geophysical studies of hydrocarbon reserves. However, data related to shear wave velocity isn't available for all wells, especially old wells and it is very important to estimate this parameter using other well logging. Hence, lots of methods have been developed to estimate these data using other available information of reservoir. In this study, after processing and removing inappropriate petrophysical data, we estimated petrophysical properties affecting shear wave velocity of the reservoir and statistical methods were used to establish relationship between effective petrophysical properties and shear wave velocity. To predict (VS), first we used empirical relationships and then multivariate regression methods and neural networks were used. Multiple regression method is a powerful method that uses correlation between available information and desired parameter. Using this method, we can identify parameters affecting estimation of shear wave velocity. Neural networks can also be trained quickly and present a stable model for predicting shear wave velocity. For this reason, this method is known as "dynamic regression" compared with multiple regression. Neural network used in this study is not like a black box because we have used the results of multiple regression that can easily modify prediction of shear wave velocity through appropriate combination of data. The same information that was intended for multiple regression was used as input in neural networks, and shear wave velocity was obtained using compressional wave velocity and well logging data (neutron, density, gamma and deep resistivity) in carbonate rocks. The results show that methods applied in this carbonate reservoir was successful, so that shear wave velocity was predicted with about 92 and 95 percents of correlation coefficient in multiple regression and neural network method, respectively. Therefore, we propose using these methods to estimate shear wave velocity in wells without this parameter. H. Akhundi et al.304

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“…A large number of techniques have been introduced in order to establish an adequate interpretation model over the past fifty years [3]. The way that petrophysical properties determination is carried out has also changed considerably over the years due to the development in logging tools and methodologies.…”

Section: Introductionmentioning

confidence: 99%

Reservoir Characterization Using Support Vector Machines

Wong

Ong

Gedeon

et al.

International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on In

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State-Of-The-Art in Permeability Determination From Well Log Data: Part 1- A Comparative Study, Model Development

Cited by 106 publications

References 9 publications

Permeability prediction from wireline logging and core data: a case study from Assam-Arakan basin

Permeability prediction from wireline logging and core data: a case study from Assam-Arakan basin

Prediction of Shear Wave Velocity Using Artificial Neural Network Technique, Multiple Regression and Petrophysical Data: A Case Study in Asmari Reservoir (SW Iran)

Reservoir Characterization Using Support Vector Machines

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