scholarly journals Direct Depth Domain Bayesian AVO Inversion

Geophysics ◽  
2021 ◽  
pp. 1-46
Author(s):  
Madhumita Sengupta ◽  
Houzhu Zhang ◽  
Yang Zhao ◽  
Mike Jervis ◽  
Dario Grana
Keyword(s):  
Reservoir Characterization ◽  
Time Window ◽  
Hybrid Approach ◽  
Spatial Location ◽  
Seismic Inversion ◽  
Reservoir Modeling ◽  
Model Parameters ◽  
New Approach ◽  
Avo Inversion ◽  
Computational Speed

We present a new approach to perform Bayesian linearized amplitude-versus-offset (AVO) inversion directly in the depth domain using non-stationary wavelets. Bayesian linearized AVO inversion, which is a hybrid approach combining physics-based models with statistical learning, has gained immense popularity in the past decade because of its superior computational speed and its ability to estimate uncertainties in the inverted model parameters. Bayesian linearized AVO inversion is typically performed on time-domain seismic data; therefore, depth-imaged seismic cannot be inverted directly using this method, and would require depth-to-time conversion before AVO inversion can be done. Subsequently, time-to-depth conversion of the inverted volumes would be required for reservoir modeling and well-placement. Domain-conversions introduce additional sources of uncertainty in the geophysical workflows. Another drawback of conventional AVO inversion techniques is that the seismic wavelet is assumed to be stationary, and this assumption leads to a restriction in the length of the time-window over which the inversion can be performed. Therefore, AVO inversion is usually restricted to a narrow time window around the target of interest, and in case multiple targets are present at different depths, multiple inversions have to be run on the same seismic volume if we use traditional AVO inversion. AVO inversion in the depth-domain using non-stationary wavelets (or point-spread functions) is a fairly recent development, and has been previously presented in an iterative formulation that is computationally intensive compared to Bayesian linearized AVO inversion. Implementing linearized Bayesian inversion directly in the depth-domain using non-stationary wavelets is a convenient new approach that takes advantage of superior computational speed and uncertainty quantification without compromising the accurate spatial location that depth imaging provides. Bringing these two schools of thought together creates a novel, unique, and powerful seismic inversion technique that can be useful for quantitative interpretation and reservoir characterization.

Application of geostatistical seismic inversion in reservoir characterization of Sapphire gas field, offshore Nile Delta, Egypt

2019 ◽  
Vol 38 (6) ◽  
pp. 474-479
Author(s):  
Mohamed G. El-Behiry ◽  
Said M. Dahroug ◽  
Mohamed Elattar
Keyword(s):  
Reservoir Characterization ◽  
Nile Delta ◽  
Seismic Inversion ◽  
Reservoir Modeling ◽  
Connectivity Analysis ◽  
Gas Field ◽  
Geostatistical Inversion ◽  
Well Data ◽  
Reservoir Connectivity ◽  
Inversion Techniques

Seismic reservoir characterization becomes challenging when reservoir thickness goes beyond the limits of seismic resolution. Geostatistical inversion techniques are being considered to overcome the resolution limitations of conventional inversion methods and to provide an intuitive understanding of subsurface uncertainty. Geostatistical inversion was applied on a highly compartmentalized area of Sapphire gas field, offshore Nile Delta, Egypt, with the aim of understanding the distribution of thin sands and their impact on reservoir connectivity. The integration of high-resolution well data with seismic partial-angle-stack volumes into geostatistical inversion has resulted in multiple elastic property realizations at the desired resolution. The multitude of inverted elastic properties are analyzed to improve reservoir characterization and reflect the inversion nonuniqueness. These property realizations are then classified into facies probability cubes and ranked based on pay sand volumes to quantify the volumetric uncertainty in static reservoir modeling. Stochastic connectivity analysis was also applied on facies models to assess the possible connected volumes. Sand connectivity analysis showed that the connected pay sand volume derived from the posterior mean of property realizations, which is analogous to deterministic inversion, is much smaller than the volumes generated by any high-frequency realization. This observation supports the role of thin interbed reservoirs in facilitating connectivity between the main sand units.

Algorithmic strategies for full waveform inversion: 1D experiments

Geophysics ◽  
2009 ◽  
Vol 74 (6) ◽  
pp. WCC37-WCC46 ◽  
Author(s):  
Carsten Burstedde ◽  
Omar Ghattas
Keyword(s):  
Total Variation ◽  
Time Window ◽  
Inequality Constraints ◽  
Seismic Inversion ◽  
General Mechanism ◽  
Total Variation Regularization ◽  
Model Parameters ◽  
Considerable Difficulty ◽  
Full Waveform ◽  
Primal Dual

Full-waveform seismic inversion, i.e., the iterative minimization of the misfit between observed seismic data and synthetic data obtained by a numerical solution of the wave equation provides a systematic, flexible, general mechanism for reconstructing earth models from observed ground motion. However, many difficulties arise for highly resolved models and the associated large-dimensional parameter spaces and high-frequency sources. First, the least-squares data-misfit functional suffers from spurious local minima, which necessitates an accurate initial guess for the smooth background model. Second, total variation regularization methods that are used to resolve sharp interfaces create significant numerical difficulties because of their nonlinearity and near-degeneracy. Third, bound constraints on continuous model parameters present considerable difficulty for commonly used active-set or interior-point methods for inequality constraints because of the infinite-dimensional nature of the parameters. Finally, common gradient-based optimization methods have difficulties scaling to the many model parameters that result when the continuous parameter fields are discretized. We have developed an optimization strategy that incorporates several techniques address these four difficulties, including grid, frequency, and time-window continuation; primal-dual methods for treating bound inequality constraints and total variation regularization; and inexact matrix-free Newton-Krylov optimization. Using this approach, several computations were performed effectively for a 1D setting with synthetic observations.

scholarly journals Data-driven method for an improved linearised AVO inversion

2020 ◽  
Author(s):  
Liping Niu ◽  
Jianhua Geng ◽  
Xinming Wu ◽  
Luanxiao Zhao ◽  
Hong Zhang
Keyword(s):  
Contrast Media ◽  
Seismic Inversion ◽  
Poor Quality ◽  
Data Driven ◽  
Inversion Method ◽  
Model Parameters ◽  
Reflection Coefficients ◽  
S Wave ◽  
Avo Inversion ◽  
Strong Contrast

Abstract Linearised approximations of the Zoeppritz equation are widely used for amplitude versus offset (AVO) forward modelling and prestack seismic inversion. However, current linearised approximations typically exhibit large errors for strong-contrast media and large incidence angles (even for those less than the critical angle), leading to poor quality estimation of elastic parameters. We therefore develop a data-driven method to improve the linearised AVO approximation, based on the concept that the inaccuracy of the conventional linearised AVO forward operator is solely responsible for generating residuals of the PP reflection coefficients calculated by the conventional linearised approximation compared with using the Zoeppritz equation. Therefore, if true model parameters from well-logging data are known, the residuals of the PP reflection coefficients can be used to estimate linear modifications of the inaccurate linearised AVO forward operators at well sites. We apply estimated linear modifications to original inaccurately linearised forward operators to obtain an improved linear AVO operator (ILAO). Because ILAOs can only be estimated at the well sites by the data-driven method, we construct spatially variant ILAOs using structure-guided interpolation for those at a distance from the well sites. Numerical example results reveal that the improved linear AVO approximation is more accurate than the Aki–Richards approximation for strong-contrast media and large incident angles. Moreover, the accuracy and resolution of the inverted P- and S-wave velocities and density using ILAOs are higher than those using the conventional Aki–Richards operators. Finally, application to the field data successfully demonstrates the feasibility and effectiveness of the improved linearised AVO inversion method.

scholarly journals A New Approach to Group Multi-Objective Optimization under Imperfect Information and Its Application to Project Portfolio Optimization

2021 ◽  
Vol 11 (10) ◽  
pp. 4575
Author(s):  
Eduardo Fernández ◽  
Nelson Rangel-Valdez ◽  
Laura Cruz-Reyes ◽  
Claudia Gomez-Santillan
Keyword(s):  
Portfolio Optimization ◽  
Imperfect Information ◽  
Model Parameters ◽  
Final Decision ◽  
Project Portfolio ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Multi Objective ◽  
Group Member

This paper addresses group multi-objective optimization under a new perspective. For each point in the feasible decision set, satisfaction or dissatisfaction from each group member is determined by a multi-criteria ordinal classification approach, based on comparing solutions with a limiting boundary between classes “unsatisfactory” and “satisfactory”. The whole group satisfaction can be maximized, finding solutions as close as possible to the ideal consensus. The group moderator is in charge of making the final decision, finding the best compromise between the collective satisfaction and dissatisfaction. Imperfect information on values of objective functions, required and available resources, and decision model parameters are handled by using interval numbers. Two different kinds of multi-criteria decision models are considered: (i) an interval outranking approach and (ii) an interval weighted-sum value function. The proposal is more general than other approaches to group multi-objective optimization since (a) some (even all) objective values may be not the same for different DMs; (b) each group member may consider their own set of objective functions and constraints; (c) objective values may be imprecise or uncertain; (d) imperfect information on resources availability and requirements may be handled; (e) each group member may have their own perception about the availability of resources and the requirement of resources per activity. An important application of the new approach is collective multi-objective project portfolio optimization. This is illustrated by solving a real size group many-objective project portfolio optimization problem using evolutionary computation tools.

scholarly journals Target Localization via Integrated and Segregated Ranging Based on RSS and TOA Measurements

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 230 ◽  
Author(s):  
Slavisa Tomic ◽  
Marko Beko
Keyword(s):  
State Of The Art ◽  
Hybrid Approach ◽  
Critical Distance ◽  
Heuristic Approach ◽  
Target Localization ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Individual Measurement ◽  
New Approach ◽  
Non Line Of Sight

This work addresses the problem of target localization in adverse non-line-of-sight (NLOS) environments by using received signal strength (RSS) and time of arrival (TOA) measurements. It is inspired by a recently published work in which authors discuss about a critical distance below and above which employing combined RSS-TOA measurements is inferior to employing RSS-only and TOA-only measurements, respectively. Here, we revise state-of-the-art estimators for the considered target localization problem and study their performance against their counterparts that employ each individual measurement exclusively. It is shown that the hybrid approach is not the best one by default. Thus, we propose a simple heuristic approach to choose the best measurement for each link, and we show that it can enhance the performance of an estimator. The new approach implicitly relies on the concept of the critical distance, but does not assume certain link parameters as given. Our simulations corroborate with findings available in the literature for line-of-sight (LOS) to a certain extent, but they indicate that more work is required for NLOS environments. Moreover, they show that the heuristic approach works well, matching or even improving the performance of the best fixed choice in all considered scenarios.

Anisotropic correction of sonic logs in wells with large relative dip

Geophysics ◽  
2008 ◽  
Vol 73 (1) ◽  
pp. E1-E5 ◽  
Author(s):  
Lev Vernik
Keyword(s):  
Reservoir Characterization ◽  
Low Frequency ◽  
P Wave ◽  
Amplitude Variation With Offset ◽  
Seismic Amplitude ◽  
Avo Inversion ◽  
Pore Pressure Prediction ◽  
Siliciclastic Rocks ◽  
Dip Angle ◽  
Sonic Logs

Seismic reservoir characterization and pore-pressure prediction projects rely heavily on the accuracy and consistency of sonic logs. Sonic data acquisition in wells with large relative dip is known to suffer from anisotropic effects related to microanisotropy of shales and thin-bed laminations of sand, silt, and shale. Nonetheless, if anisotropy parameters can be related to shale content [Formula: see text] in siliciclastic rocks, then I show that it is straightforward to compute the anisotropy correction to both compressional and shear logs using [Formula: see text] and the formation relative dip angle. The resulting rotated P-wave sonic logs can be used to enhance time-depth ties, velocity to effective stress transforms, and low-frequency models necessary for prestack seismic amplitude variation with offset (AVO) inversion.

Application of LWD Multipole Sonic for Quantitative Cement Evaluation – Well Integrity in the Norwegian Continental Shelf

2021 ◽  
Author(s):  
Subhadeep Sarkar ◽  
Mathias Horstmann ◽  
Tore Oian ◽  
Piotr Byrski ◽  
George Lawrence ◽  
...  
Keyword(s):  
Continental Shelf ◽  
Time Window ◽  
Full Range ◽  
Hybrid Approach ◽  
Bond Quality ◽  
Bond Index ◽  
Early Evaluation ◽  
Well Integrity ◽  
Wide Range ◽  
Norwegian Continental Shelf

Abstract One of the crucial components of well integrity evaluation in offshore drilling is to determine the cement bond quality assuring proper hydraulic sealing. On the Norwegian Continental Shelf (NCS) an industry standard as informative reference imposes verification of cement length and potential barriers using bonding logs. Traditionally, for the last 50 years, wireline (WL) sonic tools have been extensively used for this purpose. However, the applicability of logging-while-drilling (LWD) sonic tools for quantitative cement evaluation was explored in the recent development drilling campaign on the Dvalin Field in the Norwegian Sea, owing to significant advantages on operational efficiency and tool conveyance in any well trajectory. Cement bond evaluation from conventional peak-to-peak amplitude method has shown robust results up to bond indexes of 0.6 for LWD sonic tools. Above this limit, the casing signal is smaller than the collar signal and the amplitude method loses sensitivity to bonding. This practical challenge in the LWD realm was overcome through the inclusion of attenuation rate measurements, which responds accordingly in higher bonding environments. The two methods are used in a hybrid approach providing a full range quantitative bond index (QBI) introduced by Izuhara et al. (2017). In order to conform with local requirements related to well integrity and to ascertain the QBI potential from LWD monopole sonic, a wireline cement bond log (CBL) was acquired in the first well of the campaign for comparison. This enabled the strategic deployment of LWD QBI service in subsequent wells. LWD sonic monopole data was acquired at a controlled speed of 900ft/h. The high-fidelity waveforms were analyzed in a suitable time window and both amplitude- and attenuation-based bond indexes were derived. The combined hybrid bond index showed an excellent match with the wireline reference CBL, both in zones of high as well as lower cement bonding. The presence of formation arrivals was also in good correlation with zones of proper bonding distinguishable on the QBI results. This established the robustness of the LWD cement logging and ensured its applicability in the rest of the campaign which was carried out successfully. While the results from LWD cement evaluation service are omnidirectional, it comes with a wide range of benefits related to rig cost or conveyance in tough borehole trajectories. Early evaluation of cement quality by LWD sonic tools helps to provide adequate time for taking remedial actions if necessary. The LWD sonic as part of the drilling BHA enables this acquisition and service in non-dedicated runs, with the possibility of multiple passes for observing time-lapse effects. Also, the large sizes of LWD tools relative to the wellbore ensures a lower signal attenuation in the annulus and more effective stabilization, thereby providing a reliable bond index.

scholarly journals Recurrence time distribution and temporal clustering properties of a cellular automaton modelling landslide events

2013 ◽  
Vol 20 (6) ◽  
pp. 1071-1078 ◽  
Author(s):  
E. Piegari ◽  
R. Di Maio ◽  
A. Avella
Keyword(s):  
Time Series ◽  
Probability Distribution ◽  
Cellular Automaton ◽  
Time Window ◽  
Fano Factor ◽  
Recent Analysis ◽  
Recurrence Time ◽  
Model Parameters ◽  
Reasonable Prediction ◽  
First Time

Abstract. Reasonable prediction of landslide occurrences in a given area requires the choice of an appropriate probability distribution of recurrence time intervals. Although landslides are widespread and frequent in many parts of the world, complete databases of landslide occurrences over large periods are missing and often such natural disasters are treated as processes uncorrelated in time and, therefore, Poisson distributed. In this paper, we examine the recurrence time statistics of landslide events simulated by a cellular automaton model that reproduces well the actual frequency-size statistics of landslide catalogues. The complex time series are analysed by varying both the threshold above which the time between events is recorded and the values of the key model parameters. The synthetic recurrence time probability distribution is shown to be strongly dependent on the rate at which instability is approached, providing a smooth crossover from a power-law regime to a Weibull regime. Moreover, a Fano factor analysis shows a clear indication of different degrees of correlation in landslide time series. Such a finding supports, at least in part, a recent analysis performed for the first time of an historical landslide time series over a time window of fifty years.

Sign in / Sign up

Export Citation Format

Share Document