AVO hodograms and polarization attributes

Mahob, Patrice Nsoga; Castagna, John P.

doi:10.1190/1.1445843

Cited by 7 publications

(6 citation statements)

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“…For positive intercept value condition examples, as the angle of incident increases, the product is negative if the amplitude is decreasing and positive if the amplitude is increasing and vice versa. This method could produce a positive value to the class 3 AVO anomalies and strengthen the indication of it [12]. The statement can be explained further in the Equation (3) below:…”

Section: R(θmentioning

confidence: 95%

“…t can also be used to create scaled Poisson's ratio change where it could give a better sight to class 2 AVO anomalies. The attributes can also be expected to show a decreasing value into a reservoir zone and increasing value into a water filled porosity zone [12]. Equation ( 5) is expressed as follows: Numerous AVO attributes are derived from the combination of intercept and gradient; some of them are dot product of intercept and gradient.…”

Section: R(θmentioning

confidence: 99%

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Pay Zone Determination Using Enhanced Workflow and Neural Network

Syahputra

Hermana

Satti³

2022

Applied Sciences

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Amplitude versus offset (AVO) analysis and attributes are frequently utilized during the early stages of exploration when no well has been drilled. However, there are still some drawbacks to this method, including the fact that it involves a substantial amount of time and experience, as well as the subjectivity of manual analysis. By utilizing unsupervised learning, this process can be done more objectively and faster. Unsupervised learning can detect anomalies and identify patterns to understand more about the datasets since, at this early stage of exploration, there is still a lack of information and labelling. A type of unsupervised learning referred to as self-organizing maps (SOM) is applied in this study to delineate hydrocarbons from given AVO properties that were used to detect hydrocarbons. SOM is also used to eliminate redundancy in the selection of attributes prior to the delineation procedure. The investigation began with well log data and progressed ahead into multiple fluid conditions to evaluate the model’s ability to identify hydrocarbons. The analysis can then be extended to the seismic dataset. By combining SOM, correlation coefficient, and mean–median, a method is devised for filtering features to remove redundancy. On the hydrocarbon delineation process, the model managed to detect hydrocarbons using well log simulations and was confirmed using water saturation logs. Additionally, the model is validated using real seismic data, demonstrating a promising performance in defining probable hydrocarbons. The proposed method enables early detection of hydrocarbon content during the preliminary stage of exploration when no well is accessible.

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Section: R(θmentioning

confidence: 95%

Section: R(θmentioning

confidence: 99%

Pay Zone Determination Using Enhanced Workflow and Neural Network

Syahputra

Hermana

Satti³

2022

Applied Sciences

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“…the Nsoga Mahob and Castagna (2002) technique. For instance, as seen later on, our method highlights quantitatively AVO anomalies whether or not they are related to hydrocarbon.…”

Section: Prediction Errors and Avo Anomaliesmentioning

confidence: 99%

Coherence methods in mapping AVO anomalies and predicting P‐wave and S‐wave impedances

Barnola

White

2005

Geophysical Prospecting

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Filters for migrated offset substacks are designed by partial coherence analysis to predict ‘normal’ amplitude variation with offset (AVO) in an anomaly free area. The same prediction filters generate localized prediction errors when applied in an AVO‐anomalous interval. These prediction errors are quantitatively related to the AVO gradient anomalies in a background that is related to the minimum AVO anomaly detectable from the data. The prediction‐error section is thus used to define a reliability threshold for the identification of AVO anomalies. Coherence analysis also enables quality control of AVO analysis and inversion. For example, predictions that are non‐localized and/or do not show structural conformity may indicate spatial variations in amplitude–offset scaling, seismic wavelet or signal‐to‐noise (S/N) ratio content. Scaling and waveform variations can be identified from inspection of the prediction filters and their frequency responses. S/N ratios can be estimated via multiple coherence analysis. AVO inversion of seismic data is unstable if not constrained. However, the use of a constraint on the estimated parameters has the undesirable effect of introducing biases into the inverted results: an additional bias‐correction step is then needed to retrieve unbiased results. An alternative form of AVO inversion that avoids additional corrections is proposed. This inversion is also fast as it inverts only AVO anomalies. A spectral coherence matching technique is employed to transform a zero‐offset extrapolation or near‐offset substack into P‐wave impedance. The same technique is applied to the prediction‐error section obtained by means of partial coherence, in order to estimate S‐wave velocity to P‐wave velocity (VS/VP) ratios. Both techniques assume that accurate well ties, reliable density measurements and P‐wave and S‐wave velocity logs are available, and that impedance contrasts are not too strong. A full Zoeppritz inversion is required when impedance contrasts that are too high are encountered. An added assumption is made for the inversion to the VS/VP ratio, i.e. the Gassmann fluid‐substitution theory is valid within the reservoir area. One synthetic example and one real North Sea in‐line survey illustrate the application of the two coherence methods.

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“…Microseismic monitoring has become a common practice to characterize the distribution, orientation and extent of hydraulic fractures in tight/shale gas reservoirs. We assume that the first arrival times of the P-and/or S-wave and the azimuth angles can be derived from the microseismic wave signals by the geoscientists (Chen and Stewart, 2005;Nsoga Mahob and Castagna, 2002). We can predict these first arrival times and the azimuth angle using a forward model at an assumed event location.…”

Section: Introductionmentioning

confidence: 99%

Accurate Microseismic Event Location Inversion Using a Gradient-Based Method

Chen

Han

et al. 2012

SPE Annual Technical Conference and Exhibition

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From the microseismic data collected during multi-stage hydraulic fracturing, we may pick out the first arrival times for both P-and S-waves for each event and at each geophone. With triaxial geophones, the azimuths of the events may be derived from the hodograms. The P-and S-wave arrival times contain the distance information from the event location to the geophones as well as the event initiating time. The azimuth from hodograms gives the orientation information of the event locations. Inverting these interpreted microseismic data yields the event location parameters, which can be used to characterize the hydraulic fractures for reservoir modeling. This paper presents an efficient gradient-based microseismic event location inversion method. The forward model that is used to calculate the first arrival times is the finite-difference solution of the Eikonal equation. A novel method is devised to obtain the gradient of the first arrival times to the event location parameters in addition to the first arrival times in one forward model run. The method is applied to a realistic multilayer shale gas reservoir with a horizontal well and several stages of hydraulic fractures. The results show that the first arrival times obtained from the surface geophones yield better event location parameter estimation assuming we can pick up the events from the surface geophone signal. When the geophones are placed downhole in nearby wells, the inverted event locations are less accurate than the ones with surface geophones but can have a relative good characterization fo the fracture propagation.

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AVO hodograms and polarization attributes

Cited by 7 publications

References 0 publications

Pay Zone Determination Using Enhanced Workflow and Neural Network

Pay Zone Determination Using Enhanced Workflow and Neural Network

Coherence methods in mapping AVO anomalies and predicting P‐wave and S‐wave impedances

Accurate Microseismic Event Location Inversion Using a Gradient-Based Method

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