A pragmatic approach for prestack seismic data conditioning and quality inspection for quantitative seismic reservoir characterization

2017 ◽  
Author(s):  
Muhammad Zahid Durrani ◽  
Moin Raza Khan ◽  
Arshad Palekar ◽  
Bakhtawer Sarosh
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Quality Inspection ◽  
Pragmatic Approach ◽  
Seismic Reservoir ◽  
Data Conditioning

Seismic reservoir characterization of the Bone Spring and Wolfcamp Formations in the Delaware Basin: Challenges and uncertainty in characterization using rock physics — A case study: Part 2

2020 ◽  
Vol 8 (4) ◽  
pp. T1057-T1069
Author(s):  
Ritesh Kumar Sharma ◽  
Satinder Chopra ◽  
Larry Lines
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Well Log ◽  
Data Set ◽  
Frequency Model ◽  
Log Data ◽  
Delaware Basin ◽  
Seismic Reservoir

The discrimination of fluid content and lithology in a reservoir is important because it has a bearing on reservoir development and its management. Among other things, rock-physics analysis is usually carried out to distinguish between the lithology and fluid components of a reservoir by way of estimating the volume of clay, water saturation, and porosity using seismic data. Although these rock-physics parameters are easy to compute for conventional plays, there are many uncertainties in their estimation for unconventional plays, especially where multiple zones need to be characterized simultaneously. We have evaluated such uncertainties with reference to a data set from the Delaware Basin where the Bone Spring, Wolfcamp, Barnett, and Mississippian Formations are the prospective zones. Attempts at seismic reservoir characterization of these formations have been developed in Part 1 of this paper, where the geologic background of the area of study, the preconditioning of prestack seismic data, well-log correlation, accounting for the temporal and lateral variation in the seismic wavelets, and building of robust low-frequency model for prestack simultaneous impedance inversion were determined. We determine the challenges and the uncertainty in the characterization of the Bone Spring, Wolfcamp, Barnett, and Mississippian sections and explain how we overcame those. In the light of these uncertainties, we decide that any deterministic approach for characterization of the target formations of interest may not be appropriate and we build a case for adopting a robust statistical approach. Making use of neutron porosity and density porosity well-log data in the formations of interest, we determine how the type of shale, volume of shale, effective porosity, and lithoclassification can be carried out. Using the available log data, multimineral analysis was also carried out using a nonlinear optimization approach, which lent support to our facies classification. We then extend this exercise to derived seismic attributes for determination of the lithofacies volumes and their probabilities, together with their correlations with the facies information derived from mud log data.

Seismic reservoir characterization of Bone Spring and Wolfcamp Formations in the Delaware Basin — A case study: Part 1

2020 ◽  
Vol 8 (4) ◽  
pp. T927-T940
Author(s):  
Satinder Chopra ◽  
Ritesh Kumar Sharma ◽  
James Keay
Keyword(s):  
Reservoir Characterization ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Delaware Basin ◽  
Seismic Reservoir ◽  
Data Conditioning ◽  
Inversion Analysis ◽  
Sweet Spots

The Delaware and Midland Basins are multistacked plays with production being drawn from different zones. Of the various prospective zones in the Delaware Basin, the Bone Spring and Wolfcamp Formations are the most productive and thus are the most drilled zones. To understand the reservoirs of interest and identify the hydrocarbon sweet spots, a 3D seismic inversion project was undertaken in the northern part of the Delaware Basin in 2018. We have examined the reservoir characterization exercise for this dataset in two parts. In addition to a brief description of the geology, we evaluate the challenges faced in performing seismic inversion for characterizing multistacked plays. The key elements that lend confidence in seismic inversion and the quantitative predictions made therefrom are well-to-seismic ties, proper data conditioning, robust initial models, and adequate parameterization of inversion analysis. We examine the limitations of a conventional approach associated with these individual steps and determine how to overcome them. Later work will first elaborate on the uncertainties associated with input parameters required for executing rock-physics analysis and then evaluate the proposed robust statistical approach for defining the different lithofacies.

Processing Challenges and Solutions for Seismic Reservoir Characterization With Wide-Azimuth OBC Seismic Data, Offshore Abu Dhabi, UAE

2019 ◽  
Author(s):  
Hiroki Miyamoto ◽  
Afra Naser Almheiri ◽  
Keitaro Kojima ◽  
Toshiaki Shibasaki ◽  
Samir Bellah ◽  
...  
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Abu Dhabi ◽  
Seismic Reservoir

Seismic reservoir characterization of Utica-Point Pleasant shale with efforts at fracability evaluation — Part 2: A case study

2018 ◽  
Vol 6 (2) ◽  
pp. T325-T336 ◽  
Author(s):  
Ritesh Kumar Sharma ◽  
Satinder Chopra ◽  
James Keay ◽  
Hossein Nemati ◽  
Larry Lines
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Low Frequency ◽  
Rock Physics ◽  
Well Log ◽  
X Ray Diffraction ◽  
Impedance Inversion ◽  
Seismic Reservoir ◽  
Petrophysical Modeling

The Utica Formation in eastern Ohio possesses all the prerequisites for being a successful unconventional play. Attempts at seismic reservoir characterization of the Utica Formation have been discussed in part 1, in which, after providing the geologic background of the area of study, the preconditioning of prestack seismic data, well-log correlation, and building of robust low-frequency models for prestack simultaneous impedance inversion were explained. All these efforts were aimed at identification of sweet spots in the Utica Formation in terms of organic richness as well as brittleness. We elaborate on some aspects of that exercise, such as the challenges we faced in the determination of the total organic carbon (TOC) volume and computation of brittleness indices based on mineralogical and geomechanical considerations. The prediction of TOC in the Utica play using a methodology, in which limited seismic as well as well-log data are available, is demonstrated first. Thereafter, knowing the nonexistence of the universally accepted indicator of brittleness, mechanical along with mineralogical attempts to extract the brittleness information for the Utica play are discussed. Although an attempt is made to determine brittleness from mechanical rock-physics parameters (Young’s modulus and Poisson’s ratio) derived from seismic data, the available X-ray diffraction data and regional petrophysical modeling make it possible to determine the brittleness index based on mineralogical data and thereafter be derived from seismic data.

scholarly journals Application of Artificial Neural Network in seismic reservoir characterization: a case study from Offshore Nile Delta

2021 ◽  
Author(s):  
Adel Othman ◽  
Mohamed Fathy ◽  
Islam A. Mohamed
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Nile Delta ◽  
Reservoir Properties ◽  
Seismic Amplitude ◽  
Inversion Methods ◽  
Amplitude Data ◽  
Seismic Reservoir

AbstractThe Prediction of the reservoir characteristics from seismic amplitude data is a main challenge. Especially in the Nile Delta Basin, where the subsurface geology is complex and the reservoirs are highly heterogeneous. Modern seismic reservoir characterization methodologies are spanning around attributes analysis, deterministic and stochastic inversion methods, Amplitude Variation with Offset (AVO) interpretations, and stack rotations. These methodologies proved good outcomes in detecting the gas sand reservoirs and quantifying the reservoir properties. However, when the pre-stack seismic data is not available, most of the AVO-related inversion methods cannot be implemented. Moreover, there is no direct link between the seismic amplitude data and most of the reservoir properties, such as hydrocarbon saturation, many assumptions are imbedded and the results are questionable. Application of Artificial Neural Network (ANN) algorithms to predict the reservoir characteristics is a new emerging trend. The main advantage of the ANN algorithm over the other seismic reservoir characterization methodologies is the ability to build nonlinear relationships between the petrophysical logs and seismic data. Hence, it can be used to predict various reservoir properties in a 3D space with a reasonable amount of accuracy. We implemented the ANN method on the Sequoia gas field, Offshore Nile Delta, to predict the reservoir petrophysical properties from the seismic amplitude data. The chosen algorithm was the Probabilistic Neural Network (PNN). One well was kept apart from the analysis and used later as blind quality control to test the results.

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