Exploring Reservoir within Hugin Formation in Theta Vest Structure using 4-D Seismic and Machine Learning Approach

Abstract This paper aims to identify the oil distribution using 4-D seismic below a complex 3-D surface in Hugin Formation using machine learning and geobody detection. The exploration well 15/9-19-SR, drilled to the Theta Vest structure, was based on the interpretation of reprocessed ST8215R 3-D seismic survey data from 1991 in the Sleipner area, encountered oil in the Jurassic Hugin Formation. The drills stem test showed outstanding production capacities through time, with low water cut and low GOR. 4-D seismic has all the traditional benefits of 3-D seismic. A significant additional potential benefit is that fluid-flow processes can be directly imaged. The 4-D seismic analysis was conducted in 2012 to repeat the 3-D seismic surveys and analyze images in time-lapse mode to monitor time-varying fluid-flow processes during reservoir production. A comprehensive study of the structure and the discovery has been performed and is reported. The DNN method to predict facies far away from existing production wells by using facies log well to supervise seismic inversion created by the Seismic Color Inversion method. It can detect some oil pockets distribution and risk the well planning and the right candidate for new proposed wells.

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Dual attenuation peaks revealing mesoscopic and microscopic fluid flow in partially oil-saturated Fontainebleau sandstones

Geophysical Journal International ◽

10.1093/gji/ggaa551 ◽

2020 ◽

Vol 224 (3) ◽

pp. 1670-1683

Author(s):

Liming Zhao ◽

Genyang Tang ◽

Chao Sun ◽

Jianguo Zhao ◽

Shangxu Wang

Keyword(s):

Fluid Flow ◽

Reservoir Characterization ◽

Seismic Analysis ◽

Confining Pressure ◽

Time Lapse ◽

Seismic Attenuation ◽

Fluid Distribution ◽

Oil Viscosity ◽

Measurement Results ◽

And Shifts

SUMMARY We conducted stress–strain oscillation experiments on dry and partially oil-saturated Fontainebleau sandstone samples over the 1–2000 Hz band at different confining pressures to investigate the wave-induced fluid flow (WIFF) at mesoscopic and microscopic scales and their interaction. Three tested rock samples have similar porosity between 6 and 7 per cent and were partially saturated to different degrees with different oils. The measurement results exhibit a single or two attenuation peaks that are affected by the saturation degree, oil viscosity and confining pressure. One peak, exhibited by all samples, shifts to lower frequencies with increasing pressure, and is mainly attributed to grain contact- or microcrack-related squirt flow based on modelling of its characteristics and comparison with other experiment results for sandstones. The other peak is present at smaller frequencies and shifts to higher frequencies as the confining pressure increases, showing an opposite pressure dependence. This contrast is interpreted as the result of fluid flow patterns at different scales. We developed a dual-scale fluid flow model by incorporating the squirt flow effect into the patchy saturation model, which accounts for the interaction of WIFFs at microscopic and mesoscopic scales. This model provides a reasonable interpretation of the measurement results. Our broad-frequency-band measurements give physical evidence of WIFFs co-existing at two different scales, and combining with modelling results, it suggests that the WIFF mechanisms, related to pore microstructure and fluid distribution, interplay with each other and jointly control seismic attenuation and dispersion at reservoir conditions. These observations and modelling results are useful for quantitative seismic interpretation and reservoir characterization, specifically they have potential applications in time-lapse seismic analysis, fluid prediction and reservoir monitoring.

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Bayesian Time-lapse Difference Inversion Based on the exact Zoeppritz Equations with Blockiness Constraint

Journal of Environmental and Engineering Geophysics ◽

10.2113/jeeg19-045 ◽

2020 ◽

Vol 25 (1) ◽

pp. 89-100

Author(s):

Lin Zhou ◽

Jianping Liao ◽

Jingye Li ◽

Xiaohong Chen ◽

Tianchun Yang ◽

...

Keyword(s):

Wave Equations ◽

Oil And Gas ◽

Time Lapse ◽

Seismic Inversion ◽

Field Application ◽

Inversion Method ◽

S Wave ◽

Elastic Parameters ◽

Zoeppritz Equations ◽

Approximate Formulas

Accurately inverting changes in the reservoir elastic parameters that are caused by oil and gas exploitation is of great importance in accurately describing reservoir dynamics and enhancing recovery. Previously numerous time-lapse seismic inversion methods based on the approximate formulas of exact Zoeppritz equations or wave equations have been used to estimate these changes. However the low accuracy of calculations using approximate formulas and the significant calculation effort for the wave equations seriously limits the field application of these methods. However, these limitations can be overcome by using exact Zoeppritz equations. Therefore, we study the time-lapse seismic difference inversion method using the exact Zoeppritz equations. Firstly, the forward equation of time-lapse seismic difference data is derived based on the exact Zoeppritz equations. Secondly, the objective function based on Bayesian inversion theory is constructed using this equation, with the changes in elastic parameters assumed to obey a Gaussian distribution. In order to capture the sharp time-lapse changes of elastic parameters and further enhance the resolution of the inversion results, the blockiness constraint, which follows the differentiable Laplace distribution, is added to the prior Gaussian background model. All examples of its application show that the proposed method can obtain stable and reasonable P- and S-wave velocities and density changes from the difference data. The accuracy of estimation is higher than for existing methods, which verifies the effectiveness and feasibility of the new method. It can provide high-quality seismic inversion results for dynamic detailed reservoir description and well location during development.

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Integrated seismic and geomechanical/flow modelling study of focused fluid flow

10.5194/egusphere-egu2020-7724 ◽

2020 ◽

Author(s):

Viktoriya Yarushina ◽

Assia Lakhlifi ◽

Hongliang Wang ◽

David Connolly ◽

Magnus Wangen ◽

...

Keyword(s):

Fluid Flow ◽

Fluid Pressure ◽

Seismic Inversion ◽

Source Rocks ◽

Porous Rocks ◽

Data Set ◽

Flow Modelling ◽

Geological Past ◽

Flow Processes ◽

Preferential Pathways

<p>The improved resolution of recent seismic surveys has made seismic chimney structures a common observation in sedimentary basins worldwide and on the Norwegian Continental Shelf. Focused fluid flow in vertical chimneys is an important and poorly understood feature in a petroleum system. Oil and gas migrate through preferential pathways from source rocks to structural traps where they form reservoirs. Further migration or leakage from reservoirs leads to formation of shallow hydrocarbon accumulations and gas pockets. In some cases, leakage through preferential pathways can be traced up to the surface or to the sea floor, where it leads to formation of mud volcanoes, mounds and pockmarks. Here, we present results of an integrated case study, which is performed on a 3D seismic data set that covers an area of approximately 3000km2. The seismic sequence stratigraphic interpretation is complemented with a study of seismic fluid migration paths. Detection of seismic chimneys is a challenging task. State-of-the-art chimney cube technology based on self-educating neural networks was used to automatically identify possible structures. The results of seismic inversion in combination with available well data provided a set of surfaces distinguishing various stratigraphic layers and their properties. Obtained geological model was used as a basis for coupled geo-mechanical / fluid flow modelling that reconstructed the fluid flow processes in the geological past that lead to formation of chimney structures. Our numerical model of chimney formation is based on the two-phase theory of fluid flow through (de)compacting porous rocks. Viscous bulk rheology and strong nonlinear coupling of deforming porous rocks to fluid flow are key ingredients of the model. Chimney formation is linked to pressure build-up in the underlying reservoir. We reconstruct the fluid flow processes in the geological past that lead to formation of chimney structures and provide expectations for their present-day morphology, porosity and fluid pressure. Conditions of chimney formation, their sizes, spatial distribution and times of formation are investigated. The fate of the chimney after it has been created and its role as a fluid pathway in the present-day state is studied.</p>

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Unsupervised machine learning for time-lapse seismic studies and reservoir monitoring

Interpretation ◽

10.1190/int-2020-0176.1 ◽

2021 ◽

pp. 1-59

Author(s):

Marwa Hussein ◽

Robert R. Stewart ◽

Deborah Sacrey ◽

David H. Johnston ◽

Jonny Wu

Keyword(s):

Machine Learning ◽

Reservoir Simulation ◽

Seismic Data ◽

Water Saturation ◽

Seismic Analysis ◽

Time Lapse ◽

Seismic Attributes ◽

Rock Properties ◽

Unsupervised Machine Learning ◽

4D Seismic

Time-lapse (4D) seismic analysis plays a vital role in reservoir management and reservoir simulation model updates. However, 4D seismic data are subject to interference and tuning effects. Being able to resolve and monitor thin reservoirs of different quality can aid in optimizing infill drilling or locating bypassed hydrocarbons. Using 4D seismic data from the Maui field in the offshore Taranaki basin of New Zealand, we generate typical seismic attributes sensitive to reservoir thickness and rock properties. We find that spectral instantaneous attributes extracted from time-lapse seismic data illuminate more detailed reservoir features compared to those same attributes computed on broadband seismic data. We develop an unsupervised machine learning workflow that enables us to combine eight spectral instantaneous seismic attributes into single classification volumes for the baseline and monitor surveys using self-organizing maps (SOM). Changes in the SOM natural clusters between the baseline and monitor surveys suggest production-related changes that are caused primarily by water replacing gas as the reservoir is being swept under a strong water drive. The classification volumes also facilitate monitoring water saturation changes within thin reservoirs (ranging from very good to poor quality) as well as illuminating thin baffles. Thus, these SOM classification volumes show internal reservoir heterogeneity that can be incorporated into reservoir simulation models. Using meaningful SOM clusters, geobodies are generated for the baseline and monitor SOM classifications. The recoverable gas reserves for those geobodies are then computed and compared to production data. The SOM classifications of the Maui 4D seismic data seems to be sensitive to water saturation change and subtle pressure depletions due to gas production under a strong water drive.

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Seismic inversion by Newtonian machine learning

Geophysics ◽

10.1190/geo2019-0434.1 ◽

2020 ◽

Vol 85 (4) ◽

pp. WA185-WA200

Author(s):

Yuqing Chen ◽

Gerard T. Schuster

Keyword(s):

Neural Network ◽

Machine Learning ◽

Wave Equation ◽

Seismic Data ◽

Velocity Model ◽

Seismic Inversion ◽

Low Rank ◽

Inversion Method ◽

Model Parameters ◽

Latent Space

We present a wave-equation inversion method that inverts skeletonized seismic data for the subsurface velocity model. The skeletonized representation of the seismic traces consists of the low-rank latent-space variables predicted by a well-trained autoencoder neural network. The input to the autoencoder consists of seismic traces, and the implicit function theorem is used to determine the Fréchet derivative, i.e., the perturbation of the skeletonized data with respect to the velocity perturbation. The gradient is computed by migrating the shifted observed traces weighted by the skeletonized data residual, and the final velocity model is the one that best predicts the observed latent-space parameters. We denote this as inversion by Newtonian machine learning (NML) because it inverts for the model parameters by combining the forward and backward modeling of Newtonian wave propagation with the dimensional reduction capability of machine learning. Empirical results suggest that inversion by NML can sometimes mitigate the cycle-skipping problem of conventional full-waveform inversion (FWI). Numerical tests with synthetic and field data demonstrate the success of NML inversion in recovering a low-wavenumber approximation to the subsurface velocity model. The advantage of this method over other skeletonized data methods is that no manual picking of important features is required because the skeletal data are automatically selected by the autoencoder. The disadvantage is that the inverted velocity model has less resolution compared with the FWI result, but it can serve as a good initial model for FWI. Our most significant contribution is that we provide a general framework for using wave-equation inversion to invert skeletal data generated by any type of neural network. In other words, we have combined the deterministic modeling of Newtonian physics and the pattern matching capabilities of machine learning to invert seismic data by NML.

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Integrated time‐lapse seismic inversion for reservoir petrophysics and fluid‐flow imaging

10.1190/1.2792872 ◽

2007 ◽

Cited By ~ 1

Author(s):

T. Hong ◽

M. K. Sen ◽

P. L. Stoffa ◽

H. Klie ◽

S. G. Thomas ◽

...

Keyword(s):

Fluid Flow ◽

Time Lapse ◽

Seismic Inversion ◽

Flow Imaging

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Prediction of Reservoir Parameters Variation Based on Time-Lapse Seismic Survey

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.433-440.6370 ◽

2012 ◽

Vol 433-440 ◽

pp. 6370-6374

Author(s):

Gang Fang ◽

Xiao Hong Chen ◽

Jing Ye Li

Keyword(s):

Oil And Gas ◽

Time Lapse ◽

Seismic Inversion ◽

Gas Production ◽

Physical Models ◽

Seismic Survey ◽

Dynamic Monitoring ◽

Oil And Gas Production ◽

Fluid Saturation ◽

Unconsolidated Sand

Fluid saturation and pressure are two of most important reservoir parameters during oil and gas production scheme adjustment. A method to compute the change of fluid saturation and pressure with multi-parameters regression was presented based on time-lapse seismic inversion data. Rock physical models of unconsolidated sand rock reservoirs were determined according to the real field’s conditions to analyze how seismic attributes change with variation of reservoir parameters. The radial basis function artificial neural network which was trained by this model was used to predict saturation and effective pressure. The predicted results are of high consistency with reservoir numerical simulation, which provide valuable reference for reservoir dynamic monitoring.

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La juventud rural en Colombia: estudio crítico sobre el acceso a la formación académica superior

Criterio Jurídico Garantista ◽

10.26564/21453381.601 ◽

2016 ◽

Vol 9 (14) ◽

pp. 166-181

Author(s):

Elvis Pinzón Laitón

Keyword(s):

Higher Education ◽

Time Lapse ◽

The State ◽

Media Education ◽

Rural Sector ◽

Life Project ◽

Life Styles ◽

The Right ◽

Public Politics

El escrito demuestra que los(as) jóvenes del sector ru- ral, con relación a la educación superior, requieren de una pronta y justa atención por parte del Estado para ayudarlos(as) a superar las dificultades que afrontan una vez terminan la educación media, de modo que no vean frustrado el desarrollo de su proyecto de vida. Enfatiza en la importancia de la formulación y ejecución de polí- ticas públicas claras y adecuadas a las necesidades de los egresados de aquellos municipios distantes a las universi- dades, caso específico los de Tununguá, Boyacá, Colom- bia. Defiende la educación como el medio más importante para el desarrollo del sector rural en el país; esto implica cobertura, ayuda económica, orientación a las familias y compromiso del (la) joven para hacer parte de procesos formativos a nivel profesional en el campo de conocimien- to de su preferencia, y de esta forma acceder a otros estilos de vida para su familia, en el marco de un país que recono- ce el derecho a la igualdad.The writing shows that the young’s of the rural sector in relation to higher education, require a prompt and fair attention of the state to help to overcome the difficulties they face once, they finish their media education studies, frustrating the development of the life project, of each teenage, which is built in this time lapse. It focuses on the importance of the formulation and execution of clear public politics suitable to the necessity of the graduates of those towns distant of the universities as is the specific case of Tununguá (Boyacá, Colombia). It defends the ed- ucation line the most suitable media for the development of the rural sector in our country. It implies coverage, economic help, orientation to the families and commit- ment of the young to make part of formative processes at professional level in the knowledge field the student selects and on this way to get other life styles for their families inside the framework of a country that promul- gates the right to equality.

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