The application of pure shear wave seismic data for gas reservoir delineation

Author(s):  
Zhiwen Deng ◽  
Chengwu Li ◽  
Guowen Chen ◽  
Jing Yang ◽  
Ruizhen Wang ◽  
...  
Geophysics ◽  
1985 ◽  
Vol 50 (1) ◽  
pp. 37-48 ◽  
Author(s):  
Ross Alan Ensley

Shear waves differ from compressional waves in that their velocity is not significantly affected by changes in the fluid content of a rock. Because of this relationship, a gas‐related compressional‐wave “bright spot” or direct hydrocarbon indicator will have no comparable shear‐wave anomaly. In contrast, a lithology‐related compressional‐wave anomaly will have a corresponding shear‐wave anomaly. Thus, it is possible to use shear‐wave seismic data to evaluate compressional‐wave direct hydrocarbon indicators. This case study presents data from Myrnam, Alberta which exhibit the relationship between compressional‐ and shear‐wave seismic data over a gas reservoir and a low‐velocity coal.


2007 ◽  
Author(s):  
Zhongping Qian ◽  
Xiang‐Yang Li ◽  
Mark Chapman ◽  
Yonggang Zhang ◽  
Yanguang Wang

2021 ◽  
Author(s):  
I. Sumantri

BH field is one of the Globigerina limestone gas reservoir that exhibits strong seismic direct hydrocarbon indicator (DHI). This field is a 4-way dip faulted closure with Globigerina limestone as the main reservoir objective. The field was discovered back in 2011 by BH-1 exploration well and successfully penetrated about 350ft gross gas pay. BH-1 well was plugged and abandoned as Pliocene Globigerina limestone Mundu-Selorejo sequence gas discoveries. The laboratory analysis of sampled gas consists of 97.8% of CH4 and indicating a biogenic type of gas. This is the only exploration well drilled in this field and located on the crest of the structure. Seismic analysis both qualitative and quantitative, are common tools in delineating and characterizing reservoir. These methods usually make use of seismic data and well log collaboratively in the quest to reveal reservoir features internally. The lack of appraisal well in the area of study made the reservoir characterization process must be carried out thoroughly, incorporating several seismic datasets, both PSTM and PSDM, seismic gathers and stacks. Bounded by appraisal well limitation, this research looks into Gassmann's fluid substitution modeling, seismic forward modeling to confirm the DHI flat spot presence in the seismic, as well as seismic AVO analysis. Meanwhile, for quantitative analysis, model-based seismic post-stack inversion and simultaneous seismic pre-stack inversion were conducted in order to delineate the distribution of Globigerina limestone gas reservoir in BH Field. Through comprehensive analyses of qualitative and quantitative methods, this research may answer the challenge on how to intensively utilize seismic data to compensate the lack of appraisal well data in order to keep delivering a proper subsurface reservoir delineation.


2019 ◽  
Vol 26 (3) ◽  
pp. 434-447
Author(s):  
Amir M. S. Lala ◽  
Amr Talaat

The offshore Nile Delta Basin is considered as one of the most promising hydrocarbon provinces in Egypt, with an excellent potential for gas and condensate reserves following future exploration. Most of the discoveries in this basin, such as the reservoirs of the Upper Miocene and the Middle–Upper Pliocene, have been enabled by the use of a direct hydrocarbon indicator (DHI), based on a class III seismic amplitude v. offset (AVO) anomaly. However, there are gas-bearing formations in the Lower Pliocene that have been successfully tested where the sand did not show any seismic amplitude anomaly in full stacks or in near- and far-offset sub-stacks. The AVO analysis of this sand reservoir is referred to as AVO class II-P. Another case of a subtle AVO class I anomaly in a Lower Pliocene gas reservoir has also been tested by three wells.These variations in AVO types push us to find a new methodology to reduce the risk of unsuccessful exploration wells, mainly using seismic data. The enhanced AVO pseudo-gradient attribute (EAP) has previously been used in other studies, mainly to highlight AVO class III anomalies. However, in the present paper, we demonstrate a workflow to identify all the principal AVO classes observed in this province. Computing the EAP attribute from our data, we find that AVO class I has negative EAP values, while the other classes have positive values. Class III and classes II and II-P may be distinguished from each other as the former yields a strong positive EAP value, whereas the latter two classes yield weak EAP responses.After determining the AVO class, we define and use a new model attribute, herein termed NM, to differentiate between gas- and water-bearing formations for each class of AVO anomaly found in this province. This new method was successfully tested in many areas in the Nile Delta Basin, where it has helped to identify subtle anomalies and thereby open the gate for further exploration activities in the area.


Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. MR187-MR198 ◽  
Author(s):  
Yi Shen ◽  
Jack Dvorkin ◽  
Yunyue Li

Our goal is to accurately estimate attenuation from seismic data using model regularization in the seismic inversion workflow. One way to achieve this goal is by finding an analytical relation linking [Formula: see text] to [Formula: see text]. We derive an approximate closed-form solution relating [Formula: see text] to [Formula: see text] using rock-physics modeling. This relation is tested on well data from a clean clastic gas reservoir, of which the [Formula: see text] values are computed from the log data. Next, we create a 2D synthetic gas-reservoir section populated with [Formula: see text] and [Formula: see text] and generate respective synthetic seismograms. Now, the goal is to invert this synthetic seismic section for [Formula: see text]. If we use standard seismic inversion based solely on seismic data, the inverted attenuation model has low resolution and incorrect positioning, and it is distorted. However, adding our relation between velocity and attenuation, we obtain an attenuation model very close to the original section. This method is tested on a 2D field seismic data set from Gulf of Mexico. The resulting [Formula: see text] model matches the geologic shape of an absorption body interpreted from the seismic section. Using this [Formula: see text] model in seismic migration, we make the seismic events below the high-absorption layer clearly visible, with improved frequency content and coherency of the events.


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