Seismic detection and interpretation of porosity in Carboniferous age rocks of Kansas and Oklahoma

Geophysics ◽  
1989 ◽  
Vol 54 (11) ◽  
pp. 1371-1383 ◽  
Author(s):  
M. R. Thomasson ◽  
R. W. Kettle ◽  
R. M. Lloyd ◽  
R. K. McCormack ◽  
J. P. Lindsey
Keyword(s):  
Seismic Data ◽  
Acoustic Properties ◽  
Reservoir Quality ◽  
Seismic Modeling ◽  
Work Station ◽  
South Central ◽  
Seismic Detection ◽  
Seismic Sections

Many large Mississippian fields (5–130 MMBO) in south‐central Kansas and northern Oklahoma produce from discrete pods of porous chert and dolomite called “chat.” Chat has unusual acoustic properties that allow the porosity pods to be recognized on seismic sections. Integrated geologic and geophysical studies of two analog fields indicate that both reservoir quality and geometry can be interpreted from good quality seismic data. Seismic modeling on an interactive work station plays an important role in developing these interpretations.

scholarly journals Pitfalls and limitations in seismic attribute interpretation of tectonic features

2015 ◽  
Vol 3 (1) ◽  
pp. SB5-SB15 ◽  
Author(s):  
Kurt J. Marfurt ◽  
Tiago M. Alves
Keyword(s):  
Seismic Data ◽  
Seismic Attributes ◽  
Seismic Attribute ◽  
3D Data ◽  
Amplitude Data ◽  
Seismic Sections ◽  
Push Down ◽  
Data Coherence ◽  
Level Of Experience ◽  
Tectonic Features

Seismic attributes are routinely used to accelerate and quantify the interpretation of tectonic features in 3D seismic data. Coherence (or variance) cubes delineate the edges of megablocks and faulted strata, curvature delineates folds and flexures, while spectral components delineate lateral changes in thickness and lithology. Seismic attributes are at their best in extracting subtle and easy to overlook features on high-quality seismic data. However, seismic attributes can also exacerbate otherwise subtle effects such as acquisition footprint and velocity pull-up/push-down, as well as small processing and velocity errors in seismic imaging. As a result, the chance that an interpreter will suffer a pitfall is inversely proportional to his or her experience. Interpreters with a history of making conventional maps from vertical seismic sections will have previously encountered problems associated with acquisition, processing, and imaging. Because they know that attributes are a direct measure of the seismic amplitude data, they are not surprised that such attributes “accurately” represent these familiar errors. Less experienced interpreters may encounter these errors for the first time. Regardless of their level of experience, all interpreters are faced with increasingly larger seismic data volumes in which seismic attributes become valuable tools that aid in mapping and communicating geologic features of interest to their colleagues. In terms of attributes, structural pitfalls fall into two general categories: false structures due to seismic noise and processing errors including velocity pull-up/push-down due to lateral variations in the overburden and errors made in attribute computation by not accounting for structural dip. We evaluate these errors using 3D data volumes and find areas where present-day attributes do not provide the images we want.

On the Processing of Non-Rectangularly Sampled Multi-Dimensional Geophysical Data

1991 ◽  
Vol 02 (01) ◽  
pp. 223-226
Author(s):  
VIRGIL BARDAN
Keyword(s):  
Seismic Data ◽  
Geophysical Data ◽  
Seismic Sections

In this paper the processing of triangularly sampled 2-D seismic data is illustrated by examples of synthetic and field seismic sections.

Geostatistical Inversion in Carbonate Reservoirs to Map Reservoir Quality With High Predictability – A Case Study From Onshore Abu Dhabi

2021 ◽  
Author(s):  
S Al Naqbi ◽  
J Ahmed ◽  
J Vargas Rios ◽  
Y Utami ◽  
A Elila ◽  
...  
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Pore Volume ◽  
Rock Physics ◽  
Static Model ◽  
Reservoir Quality ◽  
Reservoir Properties ◽  
Geostatistical Inversion ◽  
High Predictability ◽  
The Impact

Abstract The Thamama group of reservoirs consist of porous carbonates laminated with tight carbonates, with pronounced lateral heterogeneities in porosity, permeability, and reservoir thickness. The main objective of our study was mapping variations and reservoir quality prediction away from well control. As the reservoirs were thin and beyond seismic resolution, it was vital that the facies and porosity be mapped in high resolution, with a high predictability, for successful placement of horizontal wells for future development of the field. We established a unified workflow of geostatistical inversion and rock physics to characterize the reservoirs. Geostatistical inversion was run in static models that were converted from depth to time domain. A robust two-way velocity model was built to map the depth grid and its zones on the time seismic data. This ensured correct placement of the predicted high-resolution elastic attributes in the depth static model. Rock physics modeling and Bayesian classification were used to convert the elastic properties into porosity and lithology (static rock-type (SRT)), which were validated in blind wells and used to rank the multiple realizations. In the geostatistical pre-stack inversion, the elastic property prediction was constrained by the seismic data and controlled by variograms, probability distributions and a guide model. The deterministic inversion was used as a guide or prior model and served as a laterally varying mean. Initially, unconstrained inversion was tested by keeping all wells as blind and the predictions were optimized by updating the input parameters. The stochastic inversion results were also frequency filtered in several frequency bands, to understand the impact of seismic data and variograms on the prediction. Finally, 30 wells were used as input, to generate 80 realizations of P-impedance, S-impedance, Vp/Vs, and density. After converting back to depth, 30 additional blind wells were used to validate the predicted porosity, with a high correlation of more than 0.8. The realizations were ranked based on the porosity predictability in blind wells combined with the pore volume histograms. Realizations with high predictability and close to the P10, P50 and P90 cases (of pore volume) were selected for further use. Based on the rock physics analysis, the predicted lithology classes were associated with the geological rock-types (SRT) for incorporation in the static model. The study presents an innovative approach to successfully integrate geostatistical inversion and rock physics with static modeling. This workflow will generate seismically constrained high-resolution reservoir properties for thin reservoirs, such as porosity and lithology, which are seamlessly mapped in the depth domain for optimized development of the field. It will also account for the uncertainties in the reservoir model through the generation of multiple equiprobable realizations or scenarios.

Using Logging and Seismic Data to Identify Boundaries Between Different OWC Units in a Deepwater Carbonate Reservoirs

2021 ◽  
Author(s):  
Kangxu Ren ◽  
Junfeng Zhao ◽  
Jian Zhao ◽  
Xilong Sun
Keyword(s):  
Seismic Data ◽  
Carbonate Rocks ◽  
Carbonate Reservoir ◽  
Carbonate Reservoirs ◽  
Sedimentary Process ◽  
Core Samples ◽  
Tight Carbonate ◽  
Porosity And Permeability ◽  
Seismic Sections ◽  
Parameters Inversion

Abstract At least three very different oil-water contacts (OWC) encountered in the deepwater, huge anticline, pre-salt carbonate reservoirs of X oilfield, Santos Basin, Brazil. The boundaries identification between different OWC units was very important to help calculating the reserves in place, which was the core factor for the development campaign. Based on analysis of wells pressure interference testing data, and interpretation of tight intervals in boreholes, predicating the pre-salt distribution of igneous rocks, intrusion baked aureoles, the silicification and the high GR carbonate rocks, the viewpoint of boundaries developed between different OWC sub-units in the lower parts of this complex carbonate reservoirs had been better understood. Core samples, logging curves, including conventional logging and other special types such as NMR, UBI and ECS, as well as the multi-parameters inversion seismic data, were adopted to confirm the tight intervals in boreholes and to predicate the possible divided boundaries between wells. In the X oilfield, hundreds of meters pre-salt carbonate reservoir had been confirmed to be laterally connected, i.e., the connected intervals including almost the whole Barra Velha Formation and/or the main parts of the Itapema Formation. However, in the middle and/or the lower sections of pre-salt target layers, the situation changed because there developed many complicated tight bodies, which were formed by intrusive diabase dykes and/or sills and the tight carbonate rocks. Many pre-salt inner-layers diabases in X oilfield had very low porosity and permeability. The tight carbonate rocks mostly developed either during early sedimentary process or by latter intrusion metamorphism and/or silicification. Tight bodies were firstly identified in drilled wells with the help of core samples and logging curves. Then, the continuous boundary were discerned on inversion seismic sections marked by wells. This paper showed the idea of coupling the different OWC units in a deepwater pre-salt carbonate play with complicated tight bodies. With the marking of wells, spatial distributions of tight layers were successfully discerned and predicated on inversion seismic sections.

Unsupervised machine learning using 3D seismic data applied to reservoir evaluation and rock type identification

2021 ◽  
pp. 1-69
Author(s):  
Marwa Hussein ◽  
Robert R. Stewart ◽  
Deborah Sacrey ◽  
Jonny Wu ◽  
Rajas Athale
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Rock Type ◽  
Seismic Attributes ◽  
Reservoir Quality ◽  
3D Seismic ◽  
Reservoir Properties ◽  
Unsupervised Machine Learning ◽  
Amplitude Data ◽  
3D Seismic Data

Net reservoir discrimination and rock type identification play vital roles in determining reservoir quality, distribution, and identification of stratigraphic baffles for optimizing drilling plans and economic petroleum recovery. Although it is challenging to discriminate small changes in reservoir properties or identify thin stratigraphic barriers below seismic resolution from conventional seismic amplitude data, we have found that seismic attributes aid in defining the reservoir architecture, properties, and stratigraphic baffles. However, analyzing numerous individual attributes is a time-consuming process and may have limitations for revealing small petrophysical changes within a reservoir. Using the Maui 3D seismic data acquired in offshore Taranaki Basin, New Zealand, we generate typical instantaneous and spectral decomposition seismic attributes that are sensitive to lithologic variations and changes in reservoir properties. Using the most common petrophysical and rock typing classification methods, the rock quality and heterogeneity of the C1 Sand reservoir are studied for four wells located within the 3D seismic volume. We find that integrating the geologic content of a combination of eight spectral instantaneous attribute volumes using an unsupervised machine-learning algorithm (self-organizing maps [SOMs]) results in a classification volume that can highlight reservoir distribution and identify stratigraphic baffles by correlating the SOM clusters with discrete net reservoir and flow-unit logs. We find that SOM classification of natural clusters of multiattribute samples in the attribute space is sensitive to subtle changes within the reservoir’s petrophysical properties. We find that SOM clusters appear to be more sensitive to porosity variations compared with lithologic changes within the reservoir. Thus, this method helps us to understand reservoir quality and heterogeneity in addition to illuminating thin reservoirs and stratigraphic baffles.

Study on Seismic Detection Applied in Mine Advance and Side

2014 ◽  
Vol 580-583 ◽  
pp. 1649-1652
Author(s):  
Yun Bing Hu ◽  
Yao Wang ◽  
Yan Qing Wu ◽  
Zi Xuan Liu
Keyword(s):  
Seismic Data ◽  
Mine Field ◽  
Qinshui Basin ◽  
Radon Transformation ◽  
Hazard Detection ◽  
Seismic Detection

This paper mainly discusses that the tunnel seismic advance detection is applied in mine geo-hazard detection. Separating the field seismic data of mine by radon transformation and migrating the data through advance way and side way, we accomplished advance and side detection simultaneously by one-time shot seismic data. Case study was carried on at Qinshui basin Yangmei No.5 mine field, and detection results mainly coincide with out-crops, demonstrating that our method can be one reference in mine geo-hazard detection.

scholarly journals Seismic Data Interpretation and Identification of Hydrocarbon-Bearing Zones of Rajian Area, Pakistan

Minerals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 891
Author(s):  
Naveed Ahmad ◽  
Sikandar Khan ◽  
Eisha Fatima Noor ◽  
Zhihui Zou ◽  
Abdullatif Al-Shuhail
Keyword(s):  
Seismic Data ◽  
Foreland Basin ◽  
Data Interpretation ◽  
Indus Basin ◽  
Subsurface Structure ◽  
Vertical Wells ◽  
Salt Range ◽  
Seismic Sections ◽  
The Given ◽  
Triangular Zone

The present study interprets the subsurface structure of the Rajian area using seismic sections and the identification of hydrocarbon-bearing zones using petrophysical analysis. The Rajian area lies within the Upper Indus Basin in the southeast (SE) of the Salt Range Potwar Foreland Basin. The marked horizons are identified using formation tops from two vertical wells. Seismic interpretation of the given 2D seismic data reveals that the study area has undergone severe distortion illustrated by thrusts and back thrusts, forming a triangular zone within the subsurface. The final trend of those structures is northwest–southeast (NW–SE), indicating that the area is part of the compressional regime. The zones interpreted by the study of hydrocarbon potential include Sakessar limestone and Khewra sandstone. Due to the unavailability of a petrophysics log within the desired investigation depths, lithology cross-plots were used for the identification of two potential hydrocarbon-bearing zones in one well at depths of 3740–3835 m (zone 1) and 4015–4100 m (zone 2). The results show that zone 2 is almost devoid of hydrocarbons, while zone 1 has an average hydrocarbon saturation of about 11%.

scholarly journals Seismic signatures of magnetic activity in solar-type stars observed by Kepler

2018 ◽  
Vol 13 (S340) ◽  
pp. 225-228
Author(s):  
A. R. G. Santos ◽  
T. L. Campante ◽  
W. J. Chaplin ◽  
M. S. Cunha ◽  
M. N. Lund ◽  
...  
Keyword(s):  
Seismic Data ◽  
Magnetic Activity ◽  
Acoustic Properties ◽  
Acoustic Modes ◽  
Solar Type ◽  
Magnetic Cycles

AbstractThe properties of the acoustic modes are sensitive to magnetic activity. The unprecedented long-term Kepler photometry, thus, allows stellar magnetic cycles to be studied through asteroseismology. We search for signatures of magnetic cycles in the seismic data of Kepler solar-type stars. We find evidence for periodic variations in the acoustic properties of about half of the 87 analysed stars. In these proceedings, we highlight the results obtained for two such stars, namely KIC 8006161 and KIC 5184732.

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