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**Theme**

**Author**

- Al-Chalabi, M. (1)
- Alvarez, Amanda Geck (1)
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**File Type**

We carry out the inversion of marine controlled-source electromagnetic data using real coded genetic algorithm to estimate the isotropic resistivity. Unlike linearized inversion methods, genetic algorithms belonging to class of stochastic methods are not limited by the requirement of the good starting models. The objective function to be optimized contains data misfit and model roughness. The regularization weight is used as a temperature like annealing parameter. This inversion is cast into a Bayesian framework where the prior distribution of the model parameters is combined with the physics of the forward problem to estimate the aposteriori probability density function in the model space. The probability distribution derived with this approach can be used to quantify the uncertainty in the estimation of vertical resistivity profile. We apply our inversion scheme on three synthetic data sets generated from horizontally stratified earth models. For all cases, our inversion estimated the resistivity to a reasonable accuracy. The results obtained from this inversion can serve as starting models for linearized/higher dimensional inversion.

Presentation Date: Monday, October 15, 2018

Start Time: 1:50:00 PM

Location: Poster Station 13

Presentation Type: Poster

amplitude, Artificial Intelligence, CSEM, CSEM data, CSEM inversion, evolutionary algorithm, fitness, genetic algorithm, geophysics, international exposition, inversion, iteration, machine learning, Mallick, marine controlled-source electromagnetic data, model parameter, optimization problem, probability, Reservoir Characterization, resistivity, roughness, synthetic data, Upstream Oil & Gas

SPE Disciplines:

Technology:

Alvarez, Pedro (Rock Solid Images) | Marin, William (Rock Solid Images) | Berrizbeitia, Juan (Rock Solid Images) | Newton, Paola (Rock Solid Images) | Bolivar, Francisco (Rock Solid Images) | Barrett, Michael (African Petroleum Corporation) | Wood, Harry (African Petroleum Corporation)

We have evaluated a case study, in which a class-1 amplitude variation with offset (AVO) turbiditic system located offshore Cote d’Ivoire, West Africa, is characterized in terms of rock properties (lithology, porosity, and fluid content) and stratigraphic elements using well-log and prestack seismic data. The methodology applied involves (1) the conditioning and modeling of well-log data to several plausible geologic scenarios at the prospect location, (2) the conditioning and inversion of prestack seismic data for P- and S-wave impedance estimation, and (3) the quantitative estimation of rock property volumes and their geologic interpretation. The approaches used for the quantitative interpretation of these rock properties were the multiattribute rotation scheme for lithology and porosity characterization and a Bayesian litho-fluid facies classification (statistical rock physics) for a probabilistic evaluation of fluid content. The result indicates how the application and integration of these different AVO- and rock-physics-based reservoir characterization workflows help us to understand key geologic stratigraphic elements of the architecture of the turbidite system and its static petrophysical characteristics (e.g., lithology, porosity, and net sand thickness). Furthermore, we found out how to quantify and interpret the risk related to the probability of finding hydrocarbon in a class-1 AVO setting using seismically derived elastic attributes, which are characterized by having a small level of sensitivity to changes in fluid saturation

Presentation Date: Tuesday, October 16, 2018

Start Time: 1:50:00 PM

Location: 209A (Anaheim Convention Center)

Presentation Type: Oral

alvarez, annual meeting 10, Artificial Intelligence, classification, estimation, facies, international exposition, interpretation, litho-fluid facies, lithology, porosity, prospect, Reservoir Characterization, rock physics, rock property, sand facies, seg seg international, Upstream Oil & Gas, Vclay, West Africa

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)

Summary This paper presents a workflow to estimate brittleness, porosity, and total organic carbon from elastic attributes. The estimation is carried out through the application of the multi-attribute rotation scheme. This method is a hybrid rock-physics/statistical approach that uses a global search algorithm to estimate a customized transform for each geologic setting in order to predict petrophysical properties from elastic attributes. After the application of this technique, customized transforms were derived for the analyzed geological setting, to estimate porosity, brittleness, total organic carbon and litho facies logs from elastic attributes. The final goal of this workflow is to apply these transforms over seismically-derived attributes to generate volumes of these petrophysical properties that can be used for reservoir characterization and production optimization.

annual meeting, application, Artificial Intelligence, brittleness, complex reservoir, correlation, correlation coefficient, estimation, international exposition, litho-facy log, Mars, optimal, petrophysical property, porosity, Reservoir Characterization, reservoir property, rotation, rotation scheme, seg seg international, shale gas, structural geology, target property, TOC, Upstream Oil & Gas

SPE Disciplines:

We carry out inversion of the marine controlled-source electromagnetic data using genetic algorithm to estimate the subsurface vertical resistivity. This inversion is cast into a Bayesian framework where the prior distribution of the model parameters is combined with the physics of the forward problem to estimate the a-posteriori probability density function in the model space. The probability distribution derived with this approach can be used to quantify the uncertainty in the estimation of vertical resistivity profile. We apply our inversion scheme on two synthetic data sets generated from two different horizontally stratified earth models. The first model had one thin resistive hydrocarbon layer between the low-resistive sediments, whereas the second model had multiple thin resistive layers. For both cases, our inversion estimated the resistivity to a reasonable accuracy. Additionally, we tested our method to invert the multi-frequency data which further improved the quality of the inverted results. The results obtained from this inversion can form a basis for higher dimensional modelling and inversions. Also, this method can be easily extended to implement the joint inversion using seismic data.

Presentation Date: Wednesday, September 27, 2017

Start Time: 2:40 PM

Location: Exhibit Hall C/D

Presentation Type: POSTER

annual meeting, Artificial Intelligence, CSEM, CSEM data, electric field, evolutionary algorithm, fitness, frequency, genetic algorithm, geophysics, inline electric, international exposition, inversion, inverted model, MacGregor, machine learning, model parameter, probability, Reservoir Characterization, resistivity, seg seg international, synthetic data, Upstream Oil & Gas

SPE Disciplines:

Technology:

Alvarez, Pedro (Rock Solid Images) | Bolívar, Francisco (Rock Solid Images) | Di Luca, Mario (Pacific Rubiales Energy) | Salinas, Trino (Pacific Rubiales Energy)

**Summary**

The multi-attribute rotation scheme (MARS) is a methodology that uses a numerical solution to estimate a transform to estimate petrophysical properties from elastic attributes. This is achieved by estimating a new attribute in the direction of maximum change of a target property in an n-dimensional Euclidian space formed by n attributes, and subsequent scaling of this attribute to the target unit properties. This approach is performed using well log-derived elastic attributes and petrophysical properties, and posteriorly applied over seismically-derived elastic attributes. In this study MARS was applied to predict a transform to estimate water saturation and total porosity from elastic attributes, using a two- and three-dimensional approach, respectively. The final goal of this workflow is to apply these transforms over seismically-derived attributes to generate volumes of these properties, which can be used in exploration and production settings for reservoir characterization and delineation, as well as soft variables in geostatistical workflows for static model generation and reserve estimation.

**Introduction**

A common way to understand the relationship between seismic attributes and a petrophysical property is by the use of rock physics templates or simply by cross-plotting well log derived elastic attributes color-coded by a petrophysical property. Both ways graphically illustrate the relationship between the elastic and petrophysical domains, which can be used to estimate reservoir properties from seismic inversion attributes. MARS is a methodology that uses a numerical solution to estimate a mathematical expression that reproduces the aforementioned phenomena. This methodology uses, as input, measured and/or rock physics-modelled well log information, to estimate a well log-derived transform between several elastic attributes and target petrophysical properties. The objective of this workflow is to apply the resultant transform over seismically-derived elastic attributes to predict the spatial distribution of petrophysical reservoir properties.

**Theory & Method **

MARS estimates a new attribute (τ) in the direction of maximum change of a target property in a n-dimensional Euclidian space formed by n attributes. We search for the maximum correlation between the target property and all the possible attributes that can be estimated via axis rotation of the basis that forms the aforementioned space.

annual meeting, Artificial Intelligence, correlation, correlation coefficient, log analysis, Mars, maximum correlation, multi-attribute rotation scheme, petrophysical property, Reservoir Characterization, reservoir property, reservoir property prediction, seismic inversion, Sw log, target property, Upstream Oil & Gas, well logging, workflow

SPE Disciplines:

Yu, Gang (BGP) | Zhang, Yusheng (BGP) | Wang, Ximing (BGP) | Liang, Xing (Zhengjiang Oilfield) | Strecker, Uwe (Rock Solid Images) | Smith, Margaret (Rock Solid Images)

Summary An integrated study of the well Zhao-104 and surrounding seismic volume within the shale gas reservoir in South China has been conducted with the objective of generating shale formation properties related to fracture orientation and intensity in the area and deriving such reservoir rock properties as data quality allows. Seismic attribute analysis of anisotropy from elliptical velocity inversion indicates that anisotropy varies horizontally and vertically, and that it is dominantly controlled by stress azimuth, which conforms to the current day stress field as independently determined from borehole breakouts. For the reservoir, it appears that the modern-day SH (N40E) orientation approximates the conjugate fracture orientation of a wrench-faulted tectonic regime; this map pattern suggests a clockwise net rotation of the stress field from time of deposition to the present-day by 40 . Very large strike-slip faults (cutting the survey) have low anisotropy. Intermediate strike-slip faults cutting the entire shale section may exhibit larger anisotropy.

Oilfield Places: North America > Canada > British Columbia > Horn River Basin > Horn River Shale (0.99)

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)

de Newton, Paola Vera (Rock Solid Images) | Alvarez, Amanda Geck (Rock Solid Images) | Vázquez, Marco (PEMEX) | Salazar, Humberto (PEMEX)

Summary Changes in rock properties, fluid content and their combination can be related to multiple seismic and EM anomalies. Rock physics models are commonly used to better understand the underlying physics of observed log responses and how they are governed by local petrophysical properties (Smith, 2011). In this study from the Gulf of Mexico, we present a case that shows a strong EM anomaly that can be tied to the rock properties at the wellbore location by generating 1D CSEM models. This multi-physics approach addresses the importance of understanding of rock physics fundamentals and their integration with other technologies, such as seismic and EM. Introduction This feasibility study aims to understand the impact of elastic and electric reservoir properties using the discovery well as the calibration well for such anomaly response in a Miocene reservoir.

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)

Hargreaves, Neil (Rock Solid Images) | Treitel, Sven (Rock Solid Images) | Smith, Maggie (Rock Solid Images)

**Summary**

If a seismic trace can be represented by a sparse model of the underlying reflection series then adjacent reflections can be resolved to a greater extent than might otherwise be possible, and in particular to a greater extent than is possible after wavelet or deconvolution processing aimed at amplifying the high frequency content of the data. The enhanced resolution can be used to derive a frequency-extended version of the input trace that contains higher useable frequencies than those obtainable by wavelet or deconvolution processing.

We show examples of a form of blind deconvolution that uses an orthogonal matching pursuits algorithm to derive a sparse reflectivity estimate as the basis for a frequency-extended version of the trace. We discuss the dependence of the output resolution on the reflection sparseness and the input bandwidth and establish some approximate limits for the degree of resolution enhancement that is available in practice. These limits need to be acknowledged in the frequency extension approach if the result is to remain a valid representation of the underlying reflectivity.

amplitude, Artificial Intelligence, bandwidth, deconvolution, frequency, frequency content, Frequency extension, inversion, reflection, reflection series, reflection sparseness, reflectivity, reflectivity estimate, Reservoir Characterization, resolution, sparse, sparse inversion, spectrum, upper frequency, Upstream Oil & Gas, wavelet

Singleton, Scott (Rock Solid Images) | Keirstead, Rob (Rock Solid Images)

This paper is the third part in a reservoir characterization series. Its objective is to demonstrate the necessity of understanding the rock property responses of a reservoir so that the project results can correctly interpreted.

The first step is to check and correct acoustic and density well log curves. For the current study a combination of Raymer for density and Greenburg-Castagna for Vs were applied in the shallow zone above the reservoir. Within the turbidite reservoir section a laminated sand fluid substitution was used to understand its behavior as fluid content varies, and a matrix substitution to understand its behavior as sand content varies. Synthetic gathers were calculated for all models using both ray traced and full waveform algorithms. These exercises showed that AVO analysis could be used to detect fluid changes in the seismic data but not for detecting sand content changes. Rock physics crossplots, however, could make this distinction.

The seismic inversion was calibrated to acoustic impedance (AI), shear impedance (SI), and Poisson’s Ratio (PR) well log curves and clearly revealed that acoustic anomalies seen in this prospect were the result of sand content changes and not the result of fluid saturation changes.

calibration, change, Crossplot, effect, international exposition, inversion, matrix, pre-stack simultaneous impedance inversion, property, Reservoir Characterization, reservoir description and dynamics, Response, rock, rock physics, sand, seismic processing and interpretation, shale, singleton, solution, Substitution, Upstream Oil & Gas, well

The demands that reservoir characterization place on seismic data far outweigh those of traditional structural interpretation. Because of this, gather conditioning is seen by many as a prerequisite to pre-stack inversion. This paper discusses three conditioning processes-signal/noise (S/N) improvement, stretch removal, and reflector alignment. It then seeks to document the improvements that these processes achieve in the gathers and in the inversion. Specifically, the gathers were measured for AVO fit using a 2-term Shuey equation and found to be improved by 20%. A comparison of wavelets extracted from the angle stacks found amplitude and phase spectra to be much more stabilized, even out to the far angle stack. The far angle stack seismic/synthetic inversion residuals showed a 40% drop in amplitude and completely different frequency and reflector character. Finally, the AI/SI cross-plot showed a much more compact signature that allowed lithology and pay discrimination. Conversely, the raw data cross-plot contained noisy data that entered into the area of the polygon where the pay signature lay. Geobodies captured from improperly conditioned data are thus (1) inflated in size, and (2) have lower impedances. These errors, in turn, lead to incorrect rock property and reserve estimations.

amplitude, angle, AVO, capture, conditioning, effect, gather, impedance, inversion, meeting, polygon, reflector, Reservoir Characterization, reservoir description and dynamics, seismic data conditioning, seismic processing and interpretation, stack, stretch, Taner, th annual international, trace, Upstream Oil & Gas, wavelet

Thank you!