The subject of seismic detection of abnormally high‐pressured formations has received a great deal of attention in exploration and production geophysics because of increasing exploration and production activities in frontier areas (such as the deepwater) and a need to lower cost without compromising safety and environment, and manage risk and uncertainty associated with very expensive drilling. The purpose of this review is to capture the “best practice” in this highly specialized discipline and document it. Pressure prediction from seismic data is based on fundamentals of science, especially those of rock physics and seismic attribute analysis. Nonetheless, since the first seismic application in the 1960s, practitioners of the technology have relied increasingly on empiricism, and the fundamental limitations of the tools applied to detect such hazardous formations were lost. The most successful approach to seismic pressure prediction is one that combines a good understanding of rock properties of subsurf...

Geopressure prediction using seismic data: Current status and the road ahead

1A predrill estimate of pore pressure can be obtained from seismic velocities using a velocity‐to–pore‐pressure transform, but the seismic velocities need to be derived using methods having sufficient resolution for well planning purposes. For a deepwater Gulf of Mexico example, significant differences are found between the velocity field obtained using reflection tomography and that obtained using a conventional method based on the Dix equation. These lead to significant differences in the predicted pore pressure. Parameters in the velocity‐to–pore‐pressure transform are estimated using seismic interval velocities and pressure data from nearby calibration wells. The uncertainty in the pore pressure prediction is analyzed by examining the spread in the predicted pore pressure obtained using parameter combinations which sample the region of parameter space consistent with the available well data. If calibration wells are not available, the ideas proposed in this paper can be used with measurements made whi...

Predrill pore-pressure prediction using seismic data

Predrill pore-pressure prediction using seismic dataSeismic Pore-Pressure Prediction

Status and progress of worldwide EOR field applications

The present invention is a method for estimating formation properties (4), by analyzing acoustic waves (20) that are emitted from and received by a bottom hole assembly (14).

Method for borehole measurement of formation properties

The South Caspian Sea is a major oil and gas producing area, characterized by thick sedimentary deposits and complex tectonic structure. Structural trends include anticlinal folds, steep dips, and mud volcanoes. Many of the large anticlinal folds contain world‐class producing fields. However, conventional processing of seismic data often fails to correctly image through the seismic “shadow zones” frequently observed in this region. These shadow zones are characterized by low‐amplitude events or low signal‐to‐noise ratio, arrival‐time delay, attenuation of high‐frequency information, and distortion of underlying reflectors. This often makes interpretation difficult to impossible.

Illuminating the shadows; tomography, attenuation, and pore-pressure processing in the South Caspian Sea

Illuminating the shadows: tomography, attenuation and pore pressure processing in the South Caspian Sea

In deepwater exploration, due to a lack of well information, seismic data become the main means of predicting reservoirs and hydrocarbons. AVO is the most popular method to detect hydrocarbons. However, factors such as pressure, lithology, and thin‐bed effects can cause false AVO effects. The deepwater Gulf of Mexico, in addition to these complex factors, has simple‐to‐complex salt sheet morphologies. The evolution of these salt sheets through time significantly influences the accumulation and migration pathways of deepwater sediments. In this article, we propose that the hydrocarbon migration pathway and seal integrity can be better understood by combining information about pore‐fluid pressure, effective stress, lithology, and seismic energy attenuation.

Deepwater reservoir prediction using seismic and geomechanical methods

In areas that lack well information, seismic data becomes the main means of detecting reservoirs and hydrocarbons. AVO is the most popular method of detection; however, factors such as pressure, lithology, and thin bed effects can cause an AVO effect which is not related to the presence of hydrocarbons. To improve the accuracy of reservoir detection, a method has been developed to combine information on pore fluid pressure, effective stress, residual pressure, and seismic Q attenuation into more reliable predictions of hydrocarbon migration pathways and seal integrity.

Detecting Reservoir Seal Integrity Using Seismic Methods

Prestack Wave-equation Processing of a Complex Synthetic Line

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Rachel Ho

Author Tools

Chat about Author