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

Geophysics ◽  
2002 ◽  
Vol 67 (6) ◽  
pp. 2012-2041 ◽  
Author(s):  
N. C. Dutta
Keyword(s):  
Seismic Data ◽  
Large Scale ◽  
Best Practice ◽  
Seismic Velocity ◽  
Rock Physics ◽  
Rock Properties ◽  
Current Status ◽  
Seismic Attribute ◽  
Attribute Analysis ◽  
Exploration And Production

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 subsurface formations with the best practice for seismic velocity analysis appropriate for rock physics applications, not for stacking purposes. With the step change that the industry has seen in the application of the modern digital computing technology to solving large‐scale exploration and production problems using seismic data, the detection of pressured formations can now be made with more confidence and better resolution. The challenge of the future is to break the communication and the “language barrier” that still exists between the seismologists, the rock physicists, and the drilling community.

Quantitative application of post-stack inversion of organic-rich shale based on rock physics linear relationship and 3D seismic data, Perth Basin, Western Australia

2014 ◽  
Vol 54 (2) ◽  
pp. 532
Author(s):  
Yazeed Altowairqi ◽  
Reza Rezaee ◽  
Milovan Urosevic
Keyword(s):  
Shale Gas ◽  
Seismic Data ◽  
Rock Physics ◽  
Seismic Inversion ◽  
3D Seismic ◽  
Physical Relationship ◽  
Seismic Properties ◽  
Attribute Analysis ◽  
Exploration And Production ◽  
Compressional Velocity

Unconventional resources such as shale gas have been an extremely important exploration and production target. To understand the seismic responses of the shale gas plays, the use of rock physical relationship is important, which is constrained with geology and formation-evaluation analysis. Since organic-rich shale seismic properties remains poorly understood, seismic inversion can be used to identify the organic-rich shale from barren shale. This approach helps identify and map spatial distributions and of the organic rich shales. This study shows the acoustic impedance (AI), which is the product of compressional velocity and density, decreases nonlinearly with increasing total organic carbon (TOC) content. TOC is obtained using Roc-Eval pyrolysis for more than 120 core shale samples for the Perth Basin. By converting the AI data to TOC precent on the seismic data, we therefore can map lateral distribution, thickness, and variation in TOC profile. This extended abstract presents a case study of the northern Perth Basin 3D seismic with application of different approaches of seismic inversion and multi-attribute analysis with the rock physical relationships.

Predict Channel Sand Body Distribution Characteristics of South Eighth District Based on RMS Amplitude Attributes & Frequency Division

2013 ◽  
Vol 734-737 ◽  
pp. 404-407 ◽  
Author(s):  
Yu Shuang Hu ◽  
Si Miao Zhu
Keyword(s):  
Seismic Data ◽  
Physical Property ◽  
Sedimentary Facies ◽  
Distribution Area ◽  
Frequency Division ◽  
Root Mean Square Amplitude ◽  
Seismic Attribute ◽  
Body Distribution ◽  
Attribute Analysis ◽  
Sand Body

A big tendency in oil industry is underestimating the heterogeneity of the reservoir and overestimating the connectivity, which results in overly optimistic estimates of the capacity. With the development of seismic attributes, we could pick up hidden reservoir lithology and physical property information from the actual seismic data, strengthen seismic data application in actual work, to ensure the objectivity of the results. In this paper, the channel sand body distribution in south eighth district of oilfield Saertu is predicted through seismic data root-mean-square amplitude and frequency division to identify sand body boundaries, predict the distribution area channel sand body characteristics successfully, which consistent with the sedimentary facies distribution. The result proves that seismic attribute analysis has good practicability in channel sand body prediction and sedimentary facies description.

scholarly journals Seismic characterisation of carbonate platforms and reservoirs: an introduction and review

2021 ◽  
pp. SP509-2021-51
Author(s):  
J. Hendry ◽  
P. Burgess ◽  
D. Hunt ◽  
X. Janson ◽  
V. Zampetti
Keyword(s):  
Seismic Data ◽  
Large Scale ◽  
Carbonate Platform ◽  
Energy Transition ◽  
Carbonate Platforms ◽  
Seismic Attribute ◽  
Sequence Stratigraphic ◽  
3D Data ◽  
Diagenetic Facies ◽  
Central Atlantic

AbstractImproved seismic data quality in the last 10–15 years, innovative use of seismic attribute combinations, extraction of geomorphological data, and new quantitative techniques, have significantly enhanced understanding of ancient carbonate platforms and processes. 3D data have become a fundamental toolkit for mapping carbonate depositional and diagenetic facies and associated flow units and barriers, giving a unique perspective how their relationships changed through time in response to tectonic, oceanographic and climatic forcing. Sophisticated predictions of lithology and porosity are being made from seismic data in reservoirs with good borehole log and core calibration for detailed integration with structural, paleoenvironmental and sequence stratigraphic interpretations. Geologists can now characterise entire carbonate platform systems and their large-scale evolution in time and space, including systems with few outcrop analogues such as the Lower Cretaceous Central Atlantic “Pre-Salt” carbonates. The papers introduced in this review illustrate opportunities, workflows, and potential pitfalls of modern carbonate seismic interpretation. They demonstrate advances in knowledge of carbonate systems achieved when geologists and geophysicists collaborate and innovate to maximise the value of seismic data from acquisition, through processing to interpretation. Future trends and developments, including machine learning and the significance of the energy transition, are briefly discussed.

Characterization of a carbonate geothermal reservoir using rock-physics-guided deep neural networks

2021 ◽  
Vol 40 (10) ◽  
pp. 751-758
Author(s):  
Fabien Allo ◽  
Jean-Philippe Coulon ◽  
Jean-Luc Formento ◽  
Romain Reboul ◽  
Laure Capar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Seismic Data ◽  
Deep Neural Networks ◽  
Synthetic Data ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Geothermal Reservoir ◽  
Rock Properties ◽  
Hybrid Techniques ◽  
Geothermal Wells

Deep neural networks (DNNs) have the potential to streamline the integration of seismic data for reservoir characterization by providing estimates of rock properties that are directly interpretable by geologists and reservoir engineers instead of elastic attributes like most standard seismic inversion methods. However, they have yet to be applied widely in the energy industry because training DNNs requires a large amount of labeled data that is rarely available. Training set augmentation, routinely used in other scientific fields such as image recognition, can address this issue and open the door to DNNs for geophysical applications. Although this approach has been explored in the past, creating realistic synthetic well and seismic data representative of the variable geology of a reservoir remains challenging. Recently introduced theory-guided techniques can help achieve this goal. A key step in these hybrid techniques is the use of theoretical rock-physics models to derive elastic pseudologs from variations of existing petrophysical logs. Rock-physics theories are already commonly relied on to generalize and extrapolate the relationship between rock and elastic properties. Therefore, they are a useful tool to generate a large catalog of alternative pseudologs representing realistic geologic variations away from the existing well locations. While not directly driven by rock physics, neural networks trained on such synthetic catalogs extract the intrinsic rock-physics relationships and are therefore capable of directly estimating rock properties from seismic amplitudes. Neural networks trained on purely synthetic data are applied to a set of 2D poststack seismic lines to characterize a geothermal reservoir located in the Dogger Formation northeast of Paris, France. The goal of the study is to determine the extent of porous and permeable layers encountered at existing geothermal wells and ultimately guide the location and design of future geothermal wells in the area.

THE INTEGRATION OF MODERN TECHNOLOGY IN GIPPSLAND'S CENTRAL FIELDS STUDY

1994 ◽  
Vol 34 (1) ◽  
pp. 513
Author(s):  
P.V.Hinton P.V.Hinton ◽  
M.G.Cousins ◽  
P.E.Symes
Keyword(s):  
Seismic Data ◽  
Field Performance ◽  
Simulation Models ◽  
Modern Technology ◽  
Depositional Environments ◽  
Seismic Attribute ◽  
Sequence Stratigraphic ◽  
Multidisciplinary Study ◽  
Attribute Analysis ◽  
Definition Of

The central fields area of the Gippsland Basin, Australia, includes the Halibut, Cobia, Fortescue, and Mackerel oil fields. These large fields are mature with about 80% of the reserves produced. During 1991 and 1992 a multidisciplinary study, integrating the latest technology, was completed to help optimise the depletion of the remaining significant reserves.A grid of 4500 km of high resolution 3D seismic data covering 191 square kilometres allowed the identification of subtle structural traps as well as better definition of sandstone truncation edges which represent the ultimate drainage points. In addition, the latest techniques in seismic attribute analysis provided insight into depositional environments, seal potential and facies distribution. Sequence stratigraphic concepts were used in combination with seismic data to build complex multi million cell 3D geological models. Reservoir simulation models were then constructed to history match past production and to predict future field performance. Facility studies were also undertaken to optimise depletion strategies.The Central Fields Depletion Study has resulted in recommendations to further develop the fields with about 80 work-overs, 50 infill wells, reduction in separator pressures, and gas lift and water handling facility upgrades. These activities are expected to increase ultimate reserves and production. Some of the recommendations have been implemented with initial results of additional drilling on Mackerel increasing platform production from 22,000 BOPD to over 50,000 BOPD. An ongoing program of additional drilling from the four platforms is expected to continue for several years.

Reservoir Prediction Under Control of Sedimentary Facies

2017 ◽  
Vol 25 (03) ◽  
pp. 1750022
Author(s):  
Xiuwei Yang ◽  
Peimin Zhu
Keyword(s):  
Seismic Data ◽  
Low Frequency ◽  
Prediction Method ◽  
Rock Physics ◽  
Real Data ◽  
Sedimentary Facies ◽  
Seismic Inversion ◽  
Rock Properties ◽  
Reservoir Prediction ◽  
Frequency Model

Acoustic impedance (AI) from seismic inversion can indicate rock properties and can be used, when combined with rock physics, to predict reservoir parameters, such as porosity. Solutions to seismic inversion problem are almost nonunique due to the limited bandwidth of seismic data. Additional constraints from well log data and geology are needed to arrive at a reasonable solution. In this paper, sedimentary facies is used to reduce the uncertainty in inversion and rock physics modeling; the results not only agree with seismic data, but also conform to geology. A reservoir prediction method, which incorporates seismic data, well logs, rock physics and sedimentary facies, is proposed. AI was first derived by constrained sparse spike inversion (CSSI) using a sedimentary facies dependent low-frequency model, and then was transformed to reservoir parameters by sequential simulation, statistical rock physics and [Formula: see text]-model. Two numerical experiments using synthetic model and real data indicated that the sedimentary facies information may help to obtain a more reasonable prediction.

Seismic‐attribute analysis utilizing multiazimuth seismic data in the offshore Nile Delta — A case study

2007 ◽  
Author(s):  
Robert Marten ◽  
Walter Rietveld ◽  
Mark Benson ◽  
Alaa Khodeir ◽  
James Keggin ◽  
...  
Keyword(s):  
Seismic Data ◽  
Nile Delta ◽  
Seismic Attribute ◽  
Attribute Analysis

Recent advances in rock physics

Geophysics ◽  
1985 ◽  
Vol 50 (12) ◽  
pp. 2480-2491 ◽  
Author(s):  
David P. Yale
Keyword(s):  
Electrical Properties ◽  
Seismic Waves ◽  
Seismic Velocity ◽  
Pore Space ◽  
Rock Physics ◽  
Rock Properties ◽  
Bright Spot ◽  
Geometrical Parameters ◽  
S Wave ◽  
Velocity Models

The need to extract more information about the subsurface from geophysical and petrophysical measurements has led to a great interest in the study of the effect of rock and fluid properties on geophysical and petrophysical measurements. Rock physics research in the last few years has been concerned with studying the effect of lithology, fluids, pore geometry, and fractures on velocity; the mechanisms of attenuation of seismic waves; the effect of anisotropy; and the electrical and dielectric properties of rocks. Understanding the interrelationships between rock properties and their expression in geophysical and petrophysical data is necessary to integrate geophysical, petrophysical, and engineering data for the enhanced exploration and characterization of petroleum reservoirs. The use of amplitude offsets, S‐wave seismic data, and full‐waveform sonic data will help in the discrimination of lithology. The effect of in situ temperatures and pressures must be taken into account, especially in fractured and unconsolidated reservoirs. Fluids have a strong effect on seismic velocities, through their compressibility, density, and chemical effects on grain and clay surfaces. S‐wave measurements should help in bright spot analysis for gas reservoirs, but theoretical considerations still show that a deep, consolidated reservoir will not have any appreciable impedance contrast due to gas. The attenuation of seismic waves has received a great deal of attention recently. The idea that Q is independent of frequency has been challenged by experimental and theoretical findings of large peaks in attenuation in the low kHz and hundreds of kHz regions. The attenuation is thought to be due to fluid‐flow mechanisms and theories suggest that there may be large attenuation due to small amounts of gas in the pore space even at seismic frequencies. Models of the effect of pores, cracks, and fractures on seismic velocity have also been studied. The thin‐crack velocity models appear to be better suited for representing fractures than pores. The anisotropy of seismic waves, especially the splitting of polarized S‐waves, may be diagnostic of sets of oriented fractures in the crust. The electrical properties of rocks are strongly dependent upon the frequency of the energy and logging is presently being done at various frequencies. The effects of frequency, fluid salinity, clays, and pore‐grain geometry on electrical properties have been studied. Models of porous media have been used extensively to study the electrical and elastic properties of rocks. There has been great interest in extracting geometrical parameters about the rock and pore space directly from microscopic observation. Other models have focused on modeling several different properties to find relationships between rock properties.

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