Well Log Analysis of Nahr Umr, Shuaiba and Zubair Formations in EB-4 well of East Baghdad Oil Field, Iraq, using Rock Physics Templates (RPTs)

RPT is a method used for classifying various lithologies and fluids from data of well logging or seismic inversion. Three Formations (Nahr Umr, Shuaiba, and Zubair Formations) were selected in the East Baghdad Oil field within well EB-4 to test the possibility of using this method. First, the interpretations of the well log and Density – Neutron cross plot were used for lithology identification, which showed that Nahr Umr and Zubair formations consist mainly of sandstone and shale, while the Shuaiba Formation consists of carbonate (dolomite and limestone). The study was also able to distinguish between the locations of hydrocarbon reservoirs using RPT. Finally, a polynomial equation was generated from the cross plot domain (AI versus Vp/Vs) to estimate one parameter from the other in these formations, and vice versa.

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Assessment of gas hydrate using pre-stack seismic inversion in Mahanadi Basin, offshore eastern India

Interpretation ◽

10.1190/int-2020-0139.1 ◽

2021 ◽

pp. 1-42

Author(s):

Maheswar Ojha ◽

Ranjana Ghosh

Keyword(s):

Gas Hydrate ◽

Rock Physics ◽

Type Equation ◽

Seismic Inversion ◽

Log Analysis ◽

Well Log ◽

Seismic Section ◽

Bottom Simulating Reflector ◽

Hydrate Saturation ◽

Well Log Analysis

The Indian National Gas Hydrate Program Expedition-01 in 2006 has discovered gas hydrate in Mahanadi offshore basin along the eastern Indian margin. However, well log analysis, pressure core measurements and Infra-Red (IR) anomalies reveal that gas hydrates are distributed as disseminated within the fine-grained sediment, unlike massive gas hydrate deposits in the Krishna-Godavari basin. 2D multi-channel seismic section, which crosses the Holes NGHP-01-9A and 19B located at about 24 km apart shows a continuous bottom-simulating reflector (BSR) along it. We aim to investigate the prospect of gas hydrate accumulation in this area by integrating well log analysis and seismic methods with rock physics modeling. First, we estimate gas hydrate saturation at these two Holes from the observed impedance using the three-phase Biot-type equation (TPBE). Then we establish a linear relationship between gas hydrate saturation and impedance contrast with respect to the water-saturated sediment. Using this established relation and impedance obtained from pre-stack inversion of seismic data, we produce a 2D gas hydrate-distribution image over the entire seismic section. Gas hydrate saturation estimated from resistivity and sonic data at well locations varies within 0-15%, which agrees well with the available pressure core measurements at Hole 19. However, the 2D map of gas hydrate distribution obtained from our method shows maximum gas hydrate saturation is about 40% just above the BSR between the CDP (common depth point) 1450 and 2850. The presence of gas-charged sediments below the BSR is one of the reasons for the strong BSR observed in the seismic section, which is depicted as low impedance in the inverted impedance section. Closed sedimentary structures above the BSR are probably obstructing the movements of free-gas upslope, for which we do not see the presence of gas hydrate throughout the seismic section above the BSR.

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Integrated Well Data And 3D Seismic Inversion Study For Reservoir Delineation And Description

Bulletin of the Geological Society of Malaysia ◽

10.7186/bgsm70202016 ◽

2020 ◽

Vol 70 (1) ◽

pp. 209-220

Author(s):

Qazi Sohail Imran ◽

◽

Numair Ahmad Siddiqui ◽

Abdul Halim Abdul Latif ◽

Yasir Bashir ◽

...

Keyword(s):

Reservoir Characterization ◽

Rock Physics ◽

Seismic Inversion ◽

Depositional Environments ◽

Well Log ◽

Reservoir Properties ◽

Rock Physics Modeling ◽

Physics Modeling ◽

And Performance ◽

Well Log Analysis

Offshore petroleum systems are often very complex and subtle because of a variety of depositional environments. Characterizing a reservoir based on conventional seismic and well-log stratigraphic analysis in intricate settings often leads to uncertainties. Drilling risks, as well as associated subsurface uncertainties can be minimized by accurate reservoir delineation. Moreover, a forecast can also be made about production and performance of a reservoir. This study is aimed to design a workflow in reservoir characterization by integrating seismic inversion, petrophysics and rock physics tools. Firstly, to define litho facies, rock physics modeling was carried out through well log analysis separately for each facies. Next, the available subsurface information is incorporated in a Bayesian engine which outputs several simulations of elastic reservoir properties, as well as their probabilities that were used for post-inversion analysis. Vast areal coverage of seismic and sparse vertical well log data was integrated by geostatistical inversion to produce acoustic impedance realizations of high-resolution. Porosity models were built later using the 3D impedance model. Lastly, reservoir bodies were identified and cross plot analysis discriminated the lithology and fluid within the bodies successfully.

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Well Log Analysis and Interpretation for Khasib,Tanuma, and Sa’di formations for Halfaya Oil Field in Missan Govenorate-Southern Iraq

Iraqi Journal of Science ◽

10.24996/ijs.2018.59.1c.9 ◽

2018 ◽

Vol 59 (1C) ◽

Keyword(s):

Oil Field ◽

Log Analysis ◽

Well Log ◽

Well Log Analysis ◽

Southern Iraq

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Geological Model of the Tight Reservoir (Sadi Reservoir-Southern of Iraq)

Journal of Engineering ◽

10.31026/j.eng.2019.06.03 ◽

2019 ◽

Vol 25 (6) ◽

pp. 30-43

Author(s):

Ameer Kadhum Noori ◽

Samaher A. Lazim ◽

Ahmed A. Ramadhan

Keyword(s):

Water Saturation ◽

Oil Field ◽

Log Analysis ◽

Well Log ◽

Geological Model ◽

Tight Reservoir ◽

Petrophysical Model ◽

Well Log Analysis ◽

Log Interpretation ◽

Available Information

A3D geological model was constructed for Al-Sadi reservoir/ Halfaya Oil Field which is discovered in 1976 and located 35 km from Amara city, southern of Iraq towards the Iraqi/ Iranian borders. Petrel 2014 was used to build the geological model. This model was created depending on the available information about the reservoir under study such as 2D seismic map, top and bottom of wells, geological data & well log analysis (CPI). However, the reservoir was sub-divided into 132x117x80 grid cells in the X, Y&Z directions respectively, in order to well represent the entire Al-Sadi reservoir. Well log interpretation (CPI) and core data for the existing 6 wells were the basis of the petrophysical model (Porosity, Water saturation, & Permeability) that were distributed for all the created grids and then upscaled.

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