Multiparameter 𝓁1 norm waveform fitting: Interpretation of Gulf of Mexico reflection seismograms

Geophysics ◽  
1999 ◽  
Vol 64 (4) ◽  
pp. 1023-1035 ◽  
Author(s):  
Hugues A. Djikpéssé ◽  
Albert Tarantola
Keyword(s):  
Gulf Of Mexico ◽  
Poisson’S Ratio ◽  
Seismic Refraction ◽  
Synthetic Seismograms ◽  
Poisson's Ratio ◽  
Inversion Method ◽  
Earth Model ◽  
Additional Parameter ◽  
Shear Properties ◽  
Waveform Fitting

Estimation of the elastic properties of the crust from surface seismic recordings is of great importance for the understanding of lithology and for the detection of mineral resources. Although in marine reflection experiments only P-waves are recorded, information on shear properties of the medium is contained in multioffset reflection seismograms. Being able to retrieve both dilatational and shear properties gives stronger constraints on the lithology. It is therefore desirable to recover isotropic elastic parameters from multioffset seismograms. Unfortunately, most classical waveform fitting methods used for extracting shear properties of the subsurface are based on a 1-D earth model assumption and on linear approximations of the wave equations. In this paper, a 2.5-D elastic waveform inversion method is used to extract the variations of acoustic impedance and Poisson’s ratio from marine multioffset reflection seismograms collected in the Gulf of Mexico area. A complete seismic profile is interpreted, including complex physical phenomena apparent in the data, such as unconsolidated sediment reflections and seismic refraction events. The amplitude of the reflections cannot be explained by one parameter related to the dilatational properties (P-impedance) only, when trying to minimize the least absolute fit between observed and synthetic seismograms. When adding an additional parameter related to shear properties (Poisson’s ratio), the fit between observed and synthetic seismograms improves. The resulting 2-D models of P-impedance and Poisson’s ratio contrasts are anticorrelated almost everywhere in depth, except where hydrocarbons are present. The estimation of physical P-impedance and Poisson’s ratio models by a full waveform fitting allows lithology characterization and, therefore, the delineation of a shale‐over‐gas sand reservoir.

scholarly journals Fluid prediction of a deep carbonate reservoir using walkaround 3D-3C vertical seismic profiling data

2019 ◽  
Author(s):  
Haohao Zhang ◽  
Jun Lu ◽  
Benchi Chen ◽  
Xuejun Ma ◽  
Zhidong Cai
Keyword(s):  
Poisson’S Ratio ◽  
Wave Impedance ◽  
Carbonate Reservoir ◽  
P Wave ◽  
Poisson's Ratio ◽  
Inversion Method ◽  
Seismic Profiling ◽  
Tahe Oilfield ◽  
Vertical Seismic Profiling ◽  
Time Migration

Abstract The considerable depth and complicated structure of the Tahe Oilfield in the Tuofutai area of China make it very difficult to delineate its Ordovician carbonate fracture-cavity reservoir. The resolution of conventional ground seismic data is inadequate to satisfy current exploitation requirements. To further improve the understanding of the carbonate fracture-cavity reservoir of the Tahe Oilfield and to provide predictions of reservoir properties that are more accurate, a walkaround 3D-3C vertical seismic profiling (VSP) survey was conducted. First, we preprocessed raw VSP data and developed a method of joint PP- and PSV-wave prestack time migration. In contrast to ground seismic imaging profiles, VSP imaging profiles have a higher resolution and wider spectrum range that provide more detailed strata information. Then, using the joint PP- and PSV-wave prestack inversion method, we obtained the PP- and PSV-wave impedance and Poisson's ratio parameters of the Ordovician carbonate reservoir. Compared with the P-wave impedance of the ground seismic inversion, we found the VSP inversion results had higher accuracy, which enabled clearer identification of the internal characteristics of the carbonate reservoir. Finally, coupled with the Poisson's ratio attribute, we predicted the distribution of favorable reservoirs and interwell connectivity. The prediction results were verified using both logging and production data. The findings of this study demonstrate the applicability of the proposed technical method for the exploration of deep carbonate fracture-cavity reservoirs.

AVO inversion of a Gulf of Mexico bright spot—A case study

Geophysics ◽  
1999 ◽  
Vol 64 (5) ◽  
pp. 1480-1491 ◽  
Author(s):  
Patrice Nsoga Mahob ◽  
John P. Castagna ◽  
Roger A. Young
Keyword(s):  
Gulf Of Mexico ◽  
Poisson’S Ratio ◽  
Poisson's Ratio ◽  
Bright Spot ◽  
Inversion Algorithm ◽  
Seismic Line ◽  
Data Set ◽  
The Real ◽  
Hydrocarbon Saturation ◽  
Initial Starting

An iterative and linearized inversion algorithm carried out in the x-t domain has been applied to a prestack seismic data set from the central Gulf of Mexico, offshore Louisiana. Sonic and density curves from a well located close to the seismic line are used to generate the initial starting models for the inversion. We tested the geologically realistic hypothesis that the starting models have an accurate impedance structure outside of the potential pay zone and that the prospective pay zone will have mechanical properties consistent with the presence or absence of hydrocarbons. The inversion, performed with starting models with pay zones with a Poisson’s ratio appropriate for 100% brine saturation or with a Poisson’s ratio intermediate between expected values for full brine and hydrocarbon saturation, does not converge to the real seismic gather. However, with a starting model having a Poisson’s ratio appropriate for hydrocarbon saturation in the target zone, there is convergence from the initial to the real seismic gather.

A Workflow to Derive Rock Mechanics Correlations and Stress Profile by the Integration of Dynamic and Static Formation Evaluation Data

2021 ◽  
Author(s):  
Ammar Qatari
Keyword(s):  
Rock Mechanics ◽  
Poisson’S Ratio ◽  
Poisson's Ratio ◽  
Earth Model ◽  
Stress Profile ◽  
Formation Evaluation ◽  
Static Data ◽  
Mechanical Earth Model ◽  
Low Uncertainty ◽  
Formation Specific

Abstract Rock mechanics utilizes empirical formulas which are based on studies of certain environments. The shortcoming of such criteria is having estimations of rock physical properties with high uncertainty and not field/formation specific. The objective of this paper is to apply a core-log integration to convert dynamic mechanical properties captured from formation evaluation logs and calibrate them with core static data to generate a continuous profile of data with low uncertainty and generate correlations applicable to the specific physical environment. To obtain proper rock mechanical correlations, building a mechanical earth model (MEM) calibrated with core data and stimulation data is essential. Multiple wells drilled in a certain sandstone field with rock mechanical physical tests are analyzed. Multi-arm caliber data is also put in use to establish knowledge about in-situ stress directions. The procedure starts with gathering and filtering acoustic slowness & shear, formation pressure, density, and oriented multi-arm caliper logs. Next, calibration of dynamic to core static mechanical data collected in the lab is established. The geomechanical analysis includes an understanding of the state of stresses in a chosen reservoir along with rock elastic and failure properties. The complied data is then integrated using different workflows to develop Mechanical Earth Model (MEM). The intended rock mechanics correlations include elastic constants (Young's Modulus and Poisson's ratio), and rock failure parameters. Once Mechanical Earth Model (MEM) is established, dynamic logging data and core static data are correlated to produce key rock mechanics elements that are field and formation specific. The correlations include Young's Modulus, Poisson's Ratio, Unconfined Compressive Strength (UCS) correlation, and Friction Angle (FANG) correlation. A range of each rock mechanic element is also highlighted for the specific environment showcasing the limits expected for collapse and fracture. Ultimately, stress profile is generated with low uncertainty highlighting magnitudes of maximum and minimum horizontal stresses along with the given interval.

scholarly journals THE IMPACT OF UNCERTAINTY ON SUBSIDENCE AND COMPACTION PREDICTION

2009 ◽  
Vol 12 (6) ◽  
pp. 84-95
Author(s):  
Dzung Quoc Ta ◽  
Al-Harthy, M. ◽  
Hunt, S ◽  
Sayers, J.
Keyword(s):  
Gulf Of Mexico ◽  
Young’S Modulus ◽  
Deep Water ◽  
Poisson’S Ratio ◽  
Young's Modulus ◽  
Fluid Pressure ◽  
Poisson's Ratio ◽  
Pore Fluid Pressure ◽  
Water Field ◽  
The Impact

This paper presents the stochastic approach using Monte Carlo simulation as applied to compaction and subsidence estimation in an offshore oil and gas deep-water field in the Gulf of Mexico. The results reveal both the impact of using probability distributions to estimate compaction and subsidence for a disk shaped-homogenous reservoir as well as taking into account Young's modulus, Poisson's ratio and the reduction of pore fluid pressure. The uncertainty reservoir model is also compared with numerical simulation commercial software - Eclipse 300. The stochastic-based simulation results confirm that the deterministic results obtained from the coupled geomechanical - fluid flow model are in the range of acceptable distribution for stochastic simulation. The sensitive analysis shown that Young's modulus has more impact on compaction than Poisson's ratio. The results also presented that values of Young's modulus in this deep-water field in Gulf of Mexico lying beyond 140,000psi are insignificant to compaction and subsidence. Based on output results of compaction and subsidence with the stochastic model, potential reservoirs presenting subsidence and compaction are described as an uncertainty range within distribution of Young's modulus, Poisson's ratio and the reduction of pore fluid pressure in large-scale regional model.

The Model for Calculating Elastic Modulus and Poisson's Ratio of Coal Body

2013 ◽  
Vol 6 (1) ◽  
pp. 36-43 ◽  
Author(s):  
Ai Chi ◽  
Li Yuwei
Keyword(s):  
Elastic Modulus ◽  
Poisson’S Ratio ◽  
Fractured Rock ◽  
Rock Block ◽  
Poisson's Ratio ◽  
Linear Elastic ◽  
Study Results ◽  
The Face ◽  
Block Face ◽  
Coal Rock

Coal body is a type of fractured rock mass in which lots of cleat fractures developed. Its mechanical properties vary with the parametric variation of coal rock block, face cleat and butt cleat. Based on the linear elastic theory and displacement equivalent principle and simplifying the face cleat and butt cleat as multi-bank penetrating and intermittent cracks, the model was established to calculate the elastic modulus and Poisson's ratio of coal body combined with cleat. By analyzing the model, it also obtained the influence of the parameter variation of coal rock block, face cleat and butt cleat on the elastic modulus and Poisson's ratio of the coal body. Study results showed that the connectivity rate of butt cleat and the distance between face cleats had a weak influence on elastic modulus of coal body. When the inclination of face cleat was 90°, the elastic modulus of coal body reached the maximal value and it equaled to the elastic modulus of coal rock block. When the inclination of face cleat was 0°, the elastic modulus of coal body was exclusively dependent on the elastic modulus of coal rock block, the normal stiffness of face cleat and the distance between them. When the distance between butt cleats or the connectivity rate of butt cleat was fixed, the Poisson's ratio of the coal body initially increased and then decreased with increasing of the face cleat inclination.

scholarly journals A Hybrid Artificial Intelligence Model to Predict the Elastic Behavior of Sandstone Rocks

2019 ◽  
Vol 11 (19) ◽  
pp. 5283 ◽  
Author(s):  
Gowida ◽  
Moussa ◽  
Elkatatny ◽  
Ali
Keyword(s):  
Poisson’S Ratio ◽  
Accurate Determination ◽  
Oil And Gas Industry ◽  
Poisson's Ratio ◽  
Elastic Behavior ◽  
Percentage Error ◽  
Ann Model ◽  
Field Development ◽  
Well Completion

Rock mechanical properties play a key role in the optimization process of engineering practices in the oil and gas industry so that better field development decisions can be made. Estimation of these properties is central in well placement, drilling programs, and well completion design. The elastic behavior of rocks can be studied by determining two main parameters: Young’s modulus and Poisson’s ratio. Accurate determination of the Poisson’s ratio helps to estimate the in-situ horizontal stresses and in turn, avoid many critical problems which interrupt drilling operations, such as pipe sticking and wellbore instability issues. Accurate Poisson’s ratio values can be experimentally determined using retrieved core samples under simulated in-situ downhole conditions. However, this technique is time-consuming and economically ineffective, requiring the development of a more effective technique. This study has developed a new generalized model to estimate static Poisson’s ratio values of sandstone rocks using a supervised artificial neural network (ANN). The developed ANN model uses well log data such as bulk density and sonic log as the input parameters to target static Poisson’s ratio values as outputs. Subsequently, the developed ANN model was transformed into a more practical and easier to use white-box mode using an ANN-based empirical equation. Core data (692 data points) and their corresponding petrophysical data were used to train and test the ANN model. The self-adaptive differential evolution (SADE) algorithm was used to fine-tune the parameters of the ANN model to obtain the most accurate results in terms of the highest correlation coefficient (R) and the lowest mean absolute percentage error (MAPE). The results obtained from the optimized ANN model show an excellent agreement with the laboratory measured static Poisson’s ratio, confirming the high accuracy of the developed model. A comparison of the developed ANN-based empirical correlation with the previously developed approaches demonstrates the superiority of the developed correlation in predicting static Poisson’s ratio values with the highest R and the lowest MAPE. The developed correlation performs in a manner far superior to other approaches when validated against unseen field data. The developed ANN-based mathematical model can be used as a robust tool to estimate static Poisson’s ratio without the need to run the ANN model.

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