scholarly journals Capture the variation of the pore pressure with different geological age from seismic inversion study in the Jaisalmer sub-basin, India

2020 ◽  
Vol 17 (6) ◽  
pp. 1556-1578
Author(s):  
Raman Chahal ◽  
Saurabh Datta Gupta
Keyword(s):  
Pore Pressure ◽  
Low Frequency ◽  
Velocity Model ◽  
Seismic Inversion ◽  
Interval Velocity ◽  
Hydrocarbon Maturation ◽  
Tectonically Active ◽  
Study Zone ◽  
Surface Geology ◽  
Geoscientific Data

AbstractGeoscientific evidence shows that various parameters such as compaction, buoyancy effect, hydrocarbon maturation, gas effect and tectonic activities control the pore pressure of sub-surface geology. Spatially controlled geoscientific data in the tectonically active areas is significantly useful for robust estimation of pre-drill pore pressure. The reservoir which is tectonically complex and pore pressure is changing frequently that circumference motivated us to conduct this study. The changes in pore pressure have been captured from the fine-scale to the broad scale in the Jaisalmer sub-basin. Pore pressure variation has been distinctly observed in pre- and post-Jurassic age based on the current study. Post-stack seismic inversion study was conducted to capturing the variation of pore pressure. Analysis of low-frequency spectrum and integrated interval velocity model provided a detailed feature of pore pressure in each compartment of the study area. Pore pressure estimated from well log data was correlated with seismic inversion based result. Based on the current study one well has been proposed where pore pressure was estimated and two distinguished trends are identified in the study zone. The approaches of the current study were analysed thoroughly and it will be highly useful in complex reservoir condition where pore pressure varies frequently.

scholarly journals Pore pressure prediction using seismic velocity modeling: case study, Sefid-Zakhor gas field in Southern Iran

2019 ◽  
Vol 10 (3) ◽  
pp. 1051-1062 ◽  
Author(s):  
Zahra Bahmaei ◽  
Erfan Hosseini
Keyword(s):  
Pore Pressure ◽  
Seismic Velocity ◽  
Velocity Model ◽  
Wellbore Stability ◽  
Well Test ◽  
Drilling Mud ◽  
Gas Field ◽  
Seismic Section ◽  
Interval Velocity ◽  
Seal Integrity

AbstractPore pressure estimation is important for both exploration and drilling projects. During the exploration phase, a prediction of pore pressure can be used to evaluate exploration risk factors including the migration of formation fluids and seal integrity. To optimize drilling decisions and well planning in abnormal pressured areas, it is essential to carry out pore pressure predictions before drilling. Mud weight and fracture gradient are essential parameters to have wellbore stability, prevent blowout, lost circulation, kick, sand production and reservoir damages. Predrill pore pressure accurate prediction allows the appropriate mud weight to be selected and allows the casing program to be optimized, thus enabling safety by preventing wellbore collapse and economic drilling by reducing the cost. The goal of this study is to estimate pore pressure relation with subsurface velocity in the Sefid-Zakhor gas field. Manufactured sonic logs are modified using the check shot interval velocity of Sefid-Zakhor well No. 1. The final acoustic impedance model is converted to the velocity model by removing density. Finally, the velocity model is converted to pore pressure using Bowers (in: IADC/SPE drilling conference proceedings, 1995) relation. The results of the pore pressure model are validated by pore pressure data obtained by the MDT well test tool. Generally, the results show the normal trend for pore pressure in the area, except in the left side of the anticline in the 2D seismic section, because of tectonic uplifting.

Prediction of Petrophysical Properties from Seismic Inversion and Neural Network: A case study

2021 ◽  
Author(s):  
Siddharth Garia ◽  
Arnab Kumar Pal ◽  
Karangat Ravi ◽  
Archana M Nair
Keyword(s):  
Neural Network ◽  
Bulk Density ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Well Log ◽  
Petrophysical Properties ◽  
Seismic Section ◽  
Frequency Model ◽  
Seismic Reflection Data ◽  
Reflection Data

<p>Seismic inversion method is widely used to characterize reservoirs and detect zones of interest, i.e., hydrocarbon-bearing zone in the subsurface by transforming seismic reflection data into quantitative subsurface rock properties. The primary aim of seismic inversion is to transform the 3D seismic section/cube into an acoustic impedance (AI) cube. The integration of this elastic attribute, i.e., AI cube with well log data, can thereafter help to establish correlations between AI and different petrophysical properties. The seismic inversion algorithm interpolates and spatially populates data/parameters of wells to the entire seismic section/cube based on the well log information. The case study presented here uses machine learning-neural network based algorithm to extract the different petrophysical properties such as porosity and bulk density from the seismic data of the Upper Assam basin, India. We analyzed three different stratigraphic  units that are established to be producing zones in this basin.</p><p> AI model is generated from the seismic reflection data with the help of colored inversion operator. Subsequently, low-frequency model is generated from the impedance data extracted from the well log information. To compensate for the band limited nature of the seismic data, this low-frequency model is added to the existing acoustic model. Thereafter, a feed-forward neural network (NN) is trained with AI as input and porosity/bulk density as target, validated with NN generated porosity/bulk density with actual porosity/bulk density from well log data. The trained network is thus tested over the entire region of interest to populate these petrophysical properties.</p><p>Three seismic zones were identified from the seismic section ranging from 681 to 1333 ms, 1528 to 1575 ms and 1771 to 1814 ms. The range of AI, porosity and bulk density were observed to be 1738 to 6000 (g/cc) * (m/s), 26 to 38% and 1.95 to 2.46 g/cc respectively. Studies conducted by researchers in the same basin yielded porosity results in the range of 10-36%. The changes in acoustic impedance, porosity and bulk density may be attributed to the changes in lithology. NN method was prioritized over other traditional statistical methods due to its ability to model any arbitrary dependency (non-linear relationships between input and target values) and also overfitting can be avoided. Hence, the workflow presented here provides an estimation of reservoir properties and is considered useful in predicting petrophysical properties for reservoir characterization, thus helping to estimate reservoir productivity.</p>

Poststack impedance inversion considering the diffractive component of the wavefield

Geophysics ◽  
2020 ◽  
Vol 85 (1) ◽  
pp. R11-R28 ◽  
Author(s):  
Kun Xiang ◽  
Evgeny Landa
Keyword(s):  
Specular Reflection ◽  
Low Frequency ◽  
Velocity Model ◽  
Forward Modeling ◽  
Small Scale ◽  
Fracture Detection ◽  
Frequency Model ◽  
New Approach ◽  
Impedance Inversion ◽  
Diffraction Imaging

Seismic diffraction waveform energy contains important information about small-scale subsurface elements, and it is complementary to specular reflection information about subsurface properties. Diffraction imaging has been used for fault, pinchout, and fracture detection. Very little research, however, has been carried out taking diffraction into account in the impedance inversion. Usually, in the standard inversion scheme, the input is the migrated data and the assumption is taken that the diffraction energy is optimally focused. This assumption is true only for a perfectly known velocity model and accurate true amplitude migration algorithm, which are rare in practice. We have developed a new approach for impedance inversion, which takes into account diffractive components of the total wavefield and uses the unmigrated input data. Forward modeling, designed for impedance inversion, includes the classical specular reflection plus asymptotic diffraction modeling schemes. The output model is composed of impedance perturbation and the low-frequency model. The impedance perturbation is estimated using the Bayesian approach and remapped to the migrated domain by the kinematic ray tracing. Our method is demonstrated using synthetic and field data in comparison with the standard inversion. Results indicate that inversion with taking into account diffraction can improve the acoustic impedance prediction in the vicinity of local reflector discontinuities.

scholarly journals Seismic velocity estimation: A deep recurrent neural-network approach

Geophysics ◽  
2019 ◽  
Vol 85 (1) ◽  
pp. U21-U29
Author(s):  
Gabriel Fabien-Ouellet ◽  
Rahul Sarkar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Model Building ◽  
Seismic Velocity ◽  
Velocity Model ◽  
Velocity Estimation ◽  
Interval Velocity ◽  
Velocity Model Building ◽  
3D Velocity Model

Applying deep learning to 3D velocity model building remains a challenge due to the sheer volume of data required to train large-scale artificial neural networks. Moreover, little is known about what types of network architectures are appropriate for such a complex task. To ease the development of a deep-learning approach for seismic velocity estimation, we have evaluated a simplified surrogate problem — the estimation of the root-mean-square (rms) and interval velocity in time from common-midpoint gathers — for 1D layered velocity models. We have developed a deep neural network, whose design was inspired by the information flow found in semblance analysis. The network replaces semblance estimation by a representation built with a deep convolutional neural network, and then it performs velocity estimation automatically with recurrent neural networks. The network is trained with synthetic data to identify primary reflection events, rms velocity, and interval velocity. For a synthetic test set containing 1D layered models, we find that rms and interval velocity are accurately estimated, with an error of less than [Formula: see text] for the rms velocity. We apply the neural network to a real 2D marine survey and obtain accurate rms velocity predictions leading to a coherent stacked section, in addition to an estimation of the interval velocity that reproduces the main structures in the stacked section. Our results provide strong evidence that neural networks can estimate velocity from seismic data and that good performance can be achieved on real data even if the training is based on synthetics. The findings for the 1D problem suggest that deep convolutional encoders and recurrent neural networks are promising components of more complex networks that can perform 2D and 3D velocity model building.

scholarly journals Drilling Optimization of Tight Sands and Shale Gas Reservoir in Jambi Sub-Basin Based on Pore Pressure Estimation Using Drilling Efficiency Mechanical Specific Energy (DEMSE) and Bowers Methods

2019 ◽  
Vol 125 ◽  
pp. 15001
Author(s):  
Benny Abraham Bungasalu ◽  
M. Syamsu Rosid ◽  
Don S. Basuki
Keyword(s):  
Pore Pressure ◽  
Specific Energy ◽  
Shale Gas ◽  
Seismic Inversion ◽  
Mechanical Specific Energy ◽  
Drilling Operations ◽  
Well Data ◽  
Tight Sand Gas ◽  
Productive Time ◽  
Pressure Analysis

The subsurface pressure analysis is used to detect the overpressure and problems in the well that will be drilled based on exploration well data. Various problems were found while drilling operations carried out on A and B wells, namely, Kick and Pipe sticking which cause a high Non-Productive Time (NPT). This research is conducted to identify the mechanism of overpressure formation in Tight Sand Gas and Shale Gas in the Jambi Sub-Basin. Furthermore, to predict pore pressure using the Drilling Efficiency and Mechanical Specific Energy (DEMSE) and Bowers method. The final result will be a 3D pore pressure cube in the area based on quantitative analysis of post-stack seismic inversion. The results of the pore pressure analysis from the wells and the 3D pore pressure model indicate that top of overpressure occurs in the Gumai Formation, then it is decreasing gradually approaching the hydrostatic pressure on the Basement. The mechanisms of overpressure are caused by under compaction, fluid expansion (kerogen maturation). The Gumai Formation and Talang Akar Formation are shale rocks so the type of mud weight that is well used is oil based mud (OBM).

A Slender-Body Theory for a Rigid Pile Under Static or Low-Frequency Axial Loading

1985 ◽  
Vol 107 (4) ◽  
pp. 433-440
Author(s):  
S. N. B. Fallou ◽  
C. C. Mei
Keyword(s):  
Pore Pressure ◽  
Low Frequency ◽  
Axial Loading ◽  
Elastic Half Space ◽  
Slender Body ◽  
Sea Waves ◽  
Numerical Work ◽  
Slender Body Theory ◽  
Phase Theory ◽  
Almost Everywhere

If the seabed is modeled as a poroelastic medium, the stresses and pore pressure induced by sea waves attacking pile-supported structures can be approximately treated by a quasi-static, single-phase theory almost everywhere except near the unsealed mudline. Therefore, the theory of a pile in an elastic half-space is fundamental. A survey of literature indicates that existing theories are only numerical; the few analytical formulas are based on either intuitive argument or interpolation of numerical work. A more systematic theory is first developed here for an axially loaded pile by employing slender-body approximation to a pair of integral equations. The results are used to calculate the pore pressure in the seabed around low-frequency loading.

Assessing uncertainties in velocity models and images with a fast nonlinear uncertainty quantification method

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. R63-R75 ◽  
Author(s):  
Gregory Ely ◽  
Alison Malcolm ◽  
Oleg V. Poliannikov
Keyword(s):  
Uncertainty Quantification ◽  
Seismic Imaging ◽  
Low Frequency ◽  
Expansion Method ◽  
Velocity Model ◽  
Image Features ◽  
Visible Image ◽  
Waveform Data ◽  
Velocity Models ◽  
Seismic Reflections

Seismic imaging is conventionally performed using noisy data and a presumably inexact velocity model. Uncertainties in the input parameters propagate directly into the final image and therefore into any quantity of interest, or qualitative interpretation, obtained from the image. We considered the problem of uncertainty quantification in velocity building and seismic imaging using Bayesian inference. Using a reduced velocity model, a fast field expansion method for simulating recorded wavefields, and the adaptive Metropolis-Hastings algorithm, we efficiently quantify velocity model uncertainty by generating multiple models consistent with low-frequency full-waveform data. A second application of Bayesian inversion to any seismic reflections present in the recorded data reconstructs the corresponding structures’ position along with its associated uncertainty. Our analysis complements rather than replaces traditional imaging because it allows us to assess the reliability of visible image features and to take that into account in subsequent interpretations.

Assisting salt model building with reflection full-waveform inversion

2019 ◽  
Vol 7 (2) ◽  
pp. SB43-SB52 ◽  
Author(s):  
Adriano Gomes ◽  
Joe Peterson ◽  
Serife Bitlis ◽  
Chengliang Fan ◽  
Robert Buehring
Keyword(s):  
Model Building ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Velocity Model ◽  
Full Waveform Inversion ◽  
Reflection Data ◽  
Full Waveform ◽  
Velocity Model Building ◽  
Wave Limit ◽  
Rabbit Ears

Inverting for salt geometry using full-waveform inversion (FWI) is a challenging task, mostly due to the lack of extremely low-frequency signal in the seismic data, the limited penetration depth of diving waves using typical acquisition offsets, and the difficulty in correctly modeling the amplitude (and kinematics) of reflection events associated with the salt boundary. However, recent advances in reflection FWI (RFWI) have allowed it to use deep reflection data, beyond the diving-wave limit, by extracting the tomographic term of the FWI reflection update, the so-called rabbit ears. Though lacking the resolution to fully resolve salt geometry, we can use RFWI updates as a guide for refinements in the salt interpretation, adding a partially data-driven element to salt velocity model building. In addition, we can use RFWI to update sediment velocities in complex regions surrounding salt, where ray-based approaches typically struggle. In reality, separating the effects of sediment velocity errors from salt geometry errors is not straightforward in many locations. Therefore, iterations of RFWI plus salt scenario tests may be necessary. Although it is still not the fully automatic method that has been envisioned for FWI, this combined approach can bring significant improvement to the subsalt image, as we examine on field data examples from the Gulf of Mexico.

An automated workflow for adjoint tomography—waveform misfits and synthetic inversions for the North Island, New Zealand

2020 ◽  
Vol 223 (3) ◽  
pp. 1461-1480
Author(s):  
Bryant Chow ◽  
Yoshihiro Kaneko ◽  
Carl Tape ◽  
Ryan Modrak ◽  
John Townend
Keyword(s):  
New Zealand ◽  
Large Scale ◽  
Cross Correlation ◽  
Phase Measurement ◽  
Workflow Management ◽  
Velocity Model ◽  
Spectral Element ◽  
Seismic Inversion ◽  
Ease Of Use ◽  
The North

SUMMARY We develop and verify an automated workflow for full-waveform tomography based on spectral element and adjoint methods. We choose the North Island, New Zealand as a study area because of its high seismicity, extensive seismic network, and the availability of a candidate ray tomography starting model. To assess the accuracy of this model, we simulated 250 regional earthquakes using a spectral element solver, and compared the resulting synthetics with recorded waveforms. In a 10–30 s passband, reasonable cross-correlation phase and amplitude misfits exist between data and synthetics, whereas at 2–30 s, waveform misalignment is severe enough that meaningful cross-correlation measurements are no longer possible. To improve the velocity model at these short periods, we created an automated inversion framework based on existing tools for signal processing, phase measurement, nonlinear optimization, and workflow management. To verify the inversion framework, we performed a realistic synthetic inversion for 3-D checkerboard structure and analyzed model recovery, misfit reduction, and waveform improvement. The results of this analysis show that the source–receiver distribution within the chosen domain is capable of resolving velocity anomalies in regions of sufficient data coverage, and of magnitudes comparable to those expected in a real seismic inversion. Along with this finding, the relative ease of use and reliability of the workflow motivates future efforts targeting a high-resolution (2–30 s), large-scale (>50 000 measurements) seismic inversion for the North Island. Updated models from such an inversion are expected to improve ground motion predictions, constrain complex velocity structures, and advance understanding of New Zealand tectonics.

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