Joint inversion of time‐lapse seismic and production data using VFSA with local thermal regulation and pilot point parameterization

2009 ◽  
Author(s):  
Armando Sena ◽  
Mrinal Sen ◽  
Paul Stoffa ◽  
Roustam Seif ◽  
Long Jin
Keyword(s):  
Joint Inversion ◽  
Time Lapse ◽  
Thermal Regulation ◽  
Production Data

Joint inversion of time‐lapse seismic and production data for Norne field

2011 ◽  
Author(s):  
Amit Suman ◽  
Juan Luis Fernández‐Martínez ◽  
Tapan Mukerji
Keyword(s):  
Joint Inversion ◽  
Time Lapse ◽  
Production Data

scholarly journals Ensemble Machine Learning Assisted Reservoir Characterization Using Field Production Data–An Offshore Field Case Study

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1052
Author(s):  
Baozhong Wang ◽  
Jyotsna Sharma ◽  
Jianhua Chen ◽  
Patricia Persaud
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Reservoir Characterization ◽  
Time Lapse ◽  
Production Data ◽  
Oil Saturation ◽  
Ensemble Machine Learning ◽  
Input Parameters ◽  
Saturation Profiles ◽  
Field Production

Estimation of fluid saturation is an important step in dynamic reservoir characterization. Machine learning techniques have been increasingly used in recent years for reservoir saturation prediction workflows. However, most of these studies require input parameters derived from cores, petrophysical logs, or seismic data, which may not always be readily available. Additionally, very few studies incorporate the production data, which is an important reflection of the dynamic reservoir properties and also typically the most frequently and reliably measured quantity throughout the life of a field. In this research, the random forest ensemble machine learning algorithm is implemented that uses the field-wide production and injection data (both measured at the surface) as the only input parameters to predict the time-lapse oil saturation profiles at well locations. The algorithm is optimized using feature selection based on feature importance score and Pearson correlation coefficient, in combination with geophysical domain-knowledge. The workflow is demonstrated using the actual field data from a structurally complex, heterogeneous, and heavily faulted offshore reservoir. The random forest model captures the trends from three and a half years of historical field production, injection, and simulated saturation data to predict future time-lapse oil saturation profiles at four deviated well locations with over 90% R-square, less than 6% Root Mean Square Error, and less than 7% Mean Absolute Percentage Error, in each case.

scholarly journals Joint Inversion of Time-Lapse Seismic Data

2019 ◽  
Author(s):  
Cesar Barajas-Olalde ◽  
Donald Adams ◽  
Lu Jin ◽  
Jun He ◽  
Nicholas Kalenze ◽  
...  
Keyword(s):  
Seismic Data ◽  
Joint Inversion ◽  
Time Lapse

Ice or rock matrix? Improved quantitative imaging of Alpine permafrost evolution through time-lapse petrophysical joint inversion

2021 ◽  
Author(s):  
Johanna Klahold ◽  
Christian Hauck ◽  
Florian Wagner
Keyword(s):  
Liquid Water ◽  
Joint Inversion ◽  
Time Lapse ◽  
Swiss Alps ◽  
Permafrost Degradation ◽  
Rock Matrix ◽  
Validation Data ◽  
Water Ice ◽  
Borehole Temperature ◽  
Ice Content

<p>Quantitative estimation of pore fractions filled with liquid water, ice and air is one of the prerequisites in many permafrost studies and forms the basis for a process-based understanding of permafrost and the hazard potential of its degradation in the context of global warming. The volumetric ice content is however difficult to retrieve, since standard borehole temperature monitoring is unable to provide any ice content estimation. Geophysical methods offer opportunities to image distributions of permafrost constituents in a non-invasive manner. A petrophysical joint inversion was recently developed to determine volumetric water, ice, air and rock contents from seismic refraction and electrical resistivity data. This approach benefits from the complementary sensitivities of seismic and electrical data to the phase change between ice and liquid water. A remaining weak point was the unresolved petrophysical ambiguity between ice and rock matrix. Within this study, the petrophysical joint inversion approach is extended along the time axis and respective temporal constraints are introduced. If the porosity (and other time-invariant properties like pore water resistivity or Archie exponents) can be assumed invariant over the considered time period, water, ice and air contents can be estimated together with a temporally constant (but spatially variable) porosity distribution. It is hypothesized that including multiple time steps in the inverse problem increases the ratio of data and parameters and leads to a more accurate distinction between ice and rock content. Based on a synthetic example and a field data set from an Alpine permafrost site (Schilthorn, Swiss Alps) it is demonstrated that the developed time-lapse petrophysical joint inversion provides physically plausible solutions, in particular improved estimates for the volumetric fractions of ice and rock. The field application is evaluated with independent validation data including thaw depths derived from borehole temperature measurements and shows generally good agreement. As opposed to the conventional petrophysical joint inversion, its time-lapse extension succeeds in providing reasonable estimates of permafrost degradation at the Schilthorn monitoring site without <em>a priori </em>constraints on the porosity model.</p>

Analysis of time-lapse seismic and production data for reservoir model classification and assessment

2018 ◽  
Vol 15 (4) ◽  
pp. 1561-1587 ◽  
Author(s):  
Rafael Souza ◽  
David Lumley ◽  
Jeffrey Shragge ◽  
Alessandra Davolio ◽  
Denis José Schiozer
Keyword(s):  
Time Lapse ◽  
Production Data ◽  
Reservoir Model ◽  
Model Classification
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