Improving seismic image resolution in a carbonate fracture cave region: A case study

Wang, Naijian; Xie, Xiao‐Bi; Duan, Meng-Chuan; Daoshan, Li; Wu, Ru‐Shan

doi:10.1190/segam2019-3215909.1

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…In the seismic detection of karsts, both vertical and horizontal resolutions are important due to the nature of the problem of unknown scale. Nijian Wang et al [35] explored the capability of seismic imaging to detect artificial karst caves, gradually increasing in height from 8 to 316 m. With the given model parameters, the velocity of 4000 m/s, and the source dominant frequency of 25 Hz, it has been shown that caves less than λ 4 (40 m) cannot be separated due to wave interference [35]. As discussed earlier, some karstic features are relatively small (up to a few meters) and fall beyond the resolution of state-of-the-art seismic methods.…”

Section: Seismic Methods Of Karst Detectionmentioning

confidence: 99%

Prediction and Early Detection of Karsts—An Overview of Methods and Technologies for Safer Drilling in Carbonates

2021

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The nature of carbonate deposition as well as diagenetic processes can cause the development of unique geological features such as cavities, vugs and fractures. These are called karsts. Encountering karsts while drilling can lead to serious consequences such as severe mud losses, drops of bottom hole assembly and gas kicks. To improve drilling safety in intervals of karstification, it is important to detect karsts as early as possible, preferably in advance. In this paper, we review methods and technologies that can be used for the prediction and early detection of karsts. In particular, we consider acoustic, resistivity, seismic and drilling-data methods. In addition to the inventions and technologies developed and published over the past 40 years, this paper identifies the advantages, limitations and gaps of these existing technologies and discusses the most promising methods for karst detection and prediction.

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Section: Seismic Methods Of Karst Detectionmentioning

confidence: 99%

Prediction and Early Detection of Karsts—An Overview of Methods and Technologies for Safer Drilling in Carbonates

2021

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“…Along with the successful case studies, the main limitation in the seismic detection of karsts is related to the problem of vertical resolution [18]. It has been shown that caves less than λ 4 (40 m (131 ft)) cannot be separated due to wave interference [19]. In certain regions in the Barents Sea, encountered caves were less than a meter and they were very dangerous for drilling [20].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Detection of Karstification Hazards While Drilling in Carbonates

2022

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The nature of carbonate deposition can cause the development of unique geological features such as cavities and vugs called karsts. Encountering karsts while drilling can lead to serious consequences. To improve drilling safety in intervals of karstification, it is important to detect karsts as early as possible. The use of state-of-the-art geophysical methods cannot guarantee early or even real-time detection of karsts or karstification zones. In this paper we demonstrate, based on an analysis of 20 wells drilled in karstified carbonates in the Barents Sea, that a karst that is dangerous for drilling is often surrounded by one or more other karstification objects, thus forming a karstification zone. These zones can be detected in real time through certain patterns in drillstring mechanics and mud flow measurements. They can serve as indicators of intervals with a high likelihood of encountering karsts. The identified patterns corresponding to various karstification objects are summarized in a table and can be used by drilling engineers. Apart from that, these patterns can also be utilized for training machine learning algorithms for the automatic detection of karstification zones.

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Paleokarst reservoirs: Efficient and flexible characterization using point-spread-function-based convolution modeling

et al. 2021

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Paleokarst originate from collapse, degradation and infill of karstified rock, and typically feature spatially heterogeneous elements such as breakdown products, sediment infills and preserved open cavities on all scales. Paleokarst may further contain aquifer or hydrocarbon reservoirs as well as pose a drilling hazard during exploration. Seismic characterization of paleokarst reservoirs therefore remains both a challenging and important task. We illustrate how the application of 2(3)D spatial convolution operators, referred to as point-spread functions (PSFs), allows for seismic modeling of complex and heterogeneous paleokarst geology at a cost equivalent to conventional repeated 1D convolution. Unlike the latter, which only considers vertical resolution effects, PSF-based convolution modeling yields simulated prestack depth migrated images accounting for 3D resolution effects both vertically and laterally caused by acquisition geometries, frequency-band limitations, and propagation effects in the overburden. We confirm the validity of the approach by a comparison of modeled results to results obtained from a published physical modeling experiment. Finally, we present four additional separate case studies to highlight the usability and flexibility of the approach by assessing different issues and challenges pertaining to characterizing and interpreting seismic features of paleokarst. Through PSF-based convolution modeling, geoscientists working with paleokarst seismic data may be better able to understand how various acquisition and modeling parameters affect seismic images of paleokarst geology.

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Improving seismic image resolution in a carbonate fracture cave region: A case study

Cited by 4 publications

References 7 publications

Prediction and Early Detection of Karsts—An Overview of Methods and Technologies for Safer Drilling in Carbonates

Prediction and Early Detection of Karsts—An Overview of Methods and Technologies for Safer Drilling in Carbonates

Real-Time Detection of Karstification Hazards While Drilling in Carbonates

Paleokarst reservoirs: Efficient and flexible characterization using point-spread-function-based convolution modeling

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