Integrated Workflow for Small Scale Reservoir Characterization Using borehole Image, NMR and Core Data

Author(s):  
Haifa Rashed Al-Muraikhi ◽  
Deepak Joshi ◽  
Asmaa Faisal Al-bloushi
2016 ◽  
Vol 1 (1) ◽  
pp. 43 ◽  
Author(s):  
Sugeng Sapto Surjono ◽  
Indra Arifianto

Hydrocarbon potential within Upper Plover Formation in the Field “A” has not been produced due to unclear in understanding of reservoir problem. This formation consists of heterogeneous reservoir rock with their own physical characteristics. Reservoir characterization has been done by applying rock typing (RT) method utilizing wireline logs data to obtain reservoir properties including clay volume, porosity, water saturation, and permeability. Rock types are classified on the basis of porosity and permeability distribution from routines core analysis (RCAL) data. Meanwhile, conventional core data is utilized to depositional environment interpretations. This study also applied neural network methods to rock types analyze for intervals reservoir without core data. The Upper Plover Formation in the study area indicates potential reservoir distributes into 7 parasequences. Their were deposited during transgressive systems in coastal environments (foreshore - offshore) with coarsening upward pattern during Middle to Late Jurassic. The porosity of reservoir ranges from 1–19 % and permeability varies from 0.01 mD to 1300 mD. Based on the facies association and its physical properties from rock typing analysis, the reservoir within Upper Plover Formation can be grouped into 4 reservoir class: Class A (Excellent), Class B (Good), Class C (Poor), and Class D (Very Poor). For further analysis, only class A-C are considered as potential reservoir, and the remain is neglected.


2021 ◽  
Author(s):  
Bernd Ruehlicke ◽  
◽  
Andras Uhrin ◽  
Zbynek Veselovsky ◽  
Markus Schlaich ◽  
...  

The Thunder Horse Field targets Middle Miocene deepwater turbiditic reservoirs. Despite of being prolific, the mapping of the ~180 m thick, partly amalgamated reservoir sandstones is challenging. Seismic quality is reduced by the presence of salt structures. The salt overburden and high formation pressure requires the use of heavy mud weights and oil-based drilling fluids, which limit the resolution and interpretation potential of borehole image logs (BHI). Halokinetic movements caused significant post-depositional deformation of the already complex gravity- driven sediment stack and the reservoir beds drape against an E–W oriented salt wall. Consequently, the assessment and removal of the structural dip component is not trivial and the evaluation of paleo-transport directions is considerably more complicated compared to undisturbed deepwater reservoirs. The intention of this paper is to bring the main results from Henry et al. (2018) into context with the eigenvector methodology from Ruehlicke et al. (2019) and to emphasize its value for reservoir characterization.


Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. S187-S200 ◽  
Author(s):  
Dmitrii Merzlikin ◽  
Sergey Fomel ◽  
Mrinal K. Sen

Diffraction imaging aims to emphasize small-scale subsurface heterogeneities, such as faults, pinch-outs, fracture swarms, channels, etc. and can help seismic reservoir characterization. The key step in diffraction imaging workflows is based on the separation procedure suppressing higher energy reflections and emphasizing diffractions, after which diffractions can be imaged independently. Separation results often contain crosstalk between reflections and diffractions and are prone to noise. We have developed an inversion scheme to reduce the crosstalk and denoise diffractions. The scheme decomposes an input full wavefield into three components: reflections, diffractions, and noise. We construct the inverted forward modeling operator as the chain of three operators: Kirchhoff modeling, plane-wave destruction, and path-summation integral filter. Reflections and diffractions have the same modeling operator. Separation of the components is done by shaping regularization. We impose sparsity constraints to extract diffractions, enforce smoothing along dominant local event slopes to restore reflections, and suppress the crosstalk between the components by local signal-and-noise orthogonalization. Synthetic- and field-data examples confirm the effectiveness of the proposed method.


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