An integrated seismic reservoir characterization workflow to predict hydrocarbon production capacity in unconventional plays

2013 ◽  
Vol 1 (2) ◽  
pp. SB15-SB25
Author(s):  
Gorka Garcia Leiceaga ◽  
Mark Norton ◽  
Joël Le Calvez
Keyword(s):  
Elastic Properties ◽  
Reservoir Characterization ◽  
Production Capacity ◽  
Rock Physics ◽  
Well Log ◽  
Hydrocarbon Production ◽  
Shale Formations ◽  
Data Inversion ◽  
Seismic Reservoir ◽  
Petrophysical Evaluation

Seismic-derived elastic properties may be used to help evaluate hydrocarbon production capacity in unconventional plays such as tight or shale formations. By combining prestack seismic and well log data, inversion-based volumes of elastic properties may be produced. Moreover, a petrophysical evaluation and rock physics analysis may be carried out, thus leading to a spatial distribution of hydrocarbon production capacity. The result obtained is corroborated with the available well information, confirming our ability to accurately predict hydrocarbon production capacity in unconventional plays.

Seismic reservoir characterization of the Bone Spring and Wolfcamp Formations in the Delaware Basin: Challenges and uncertainty in characterization using rock physics — A case study: Part 2

2020 ◽  
Vol 8 (4) ◽  
pp. T1057-T1069
Author(s):  
Ritesh Kumar Sharma ◽  
Satinder Chopra ◽  
Larry Lines
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Well Log ◽  
Data Set ◽  
Frequency Model ◽  
Log Data ◽  
Delaware Basin ◽  
Seismic Reservoir

The discrimination of fluid content and lithology in a reservoir is important because it has a bearing on reservoir development and its management. Among other things, rock-physics analysis is usually carried out to distinguish between the lithology and fluid components of a reservoir by way of estimating the volume of clay, water saturation, and porosity using seismic data. Although these rock-physics parameters are easy to compute for conventional plays, there are many uncertainties in their estimation for unconventional plays, especially where multiple zones need to be characterized simultaneously. We have evaluated such uncertainties with reference to a data set from the Delaware Basin where the Bone Spring, Wolfcamp, Barnett, and Mississippian Formations are the prospective zones. Attempts at seismic reservoir characterization of these formations have been developed in Part 1 of this paper, where the geologic background of the area of study, the preconditioning of prestack seismic data, well-log correlation, accounting for the temporal and lateral variation in the seismic wavelets, and building of robust low-frequency model for prestack simultaneous impedance inversion were determined. We determine the challenges and the uncertainty in the characterization of the Bone Spring, Wolfcamp, Barnett, and Mississippian sections and explain how we overcame those. In the light of these uncertainties, we decide that any deterministic approach for characterization of the target formations of interest may not be appropriate and we build a case for adopting a robust statistical approach. Making use of neutron porosity and density porosity well-log data in the formations of interest, we determine how the type of shale, volume of shale, effective porosity, and lithoclassification can be carried out. Using the available log data, multimineral analysis was also carried out using a nonlinear optimization approach, which lent support to our facies classification. We then extend this exercise to derived seismic attributes for determination of the lithofacies volumes and their probabilities, together with their correlations with the facies information derived from mud log data.

Seismic reservoir characterization of Utica-Point Pleasant shale with efforts at fracability evaluation — Part 2: A case study

2018 ◽  
Vol 6 (2) ◽  
pp. T325-T336 ◽  
Author(s):  
Ritesh Kumar Sharma ◽  
Satinder Chopra ◽  
James Keay ◽  
Hossein Nemati ◽  
Larry Lines
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Low Frequency ◽  
Rock Physics ◽  
Well Log ◽  
X Ray Diffraction ◽  
Impedance Inversion ◽  
Seismic Reservoir ◽  
Petrophysical Modeling

The Utica Formation in eastern Ohio possesses all the prerequisites for being a successful unconventional play. Attempts at seismic reservoir characterization of the Utica Formation have been discussed in part 1, in which, after providing the geologic background of the area of study, the preconditioning of prestack seismic data, well-log correlation, and building of robust low-frequency models for prestack simultaneous impedance inversion were explained. All these efforts were aimed at identification of sweet spots in the Utica Formation in terms of organic richness as well as brittleness. We elaborate on some aspects of that exercise, such as the challenges we faced in the determination of the total organic carbon (TOC) volume and computation of brittleness indices based on mineralogical and geomechanical considerations. The prediction of TOC in the Utica play using a methodology, in which limited seismic as well as well-log data are available, is demonstrated first. Thereafter, knowing the nonexistence of the universally accepted indicator of brittleness, mechanical along with mineralogical attempts to extract the brittleness information for the Utica play are discussed. Although an attempt is made to determine brittleness from mechanical rock-physics parameters (Young’s modulus and Poisson’s ratio) derived from seismic data, the available X-ray diffraction data and regional petrophysical modeling make it possible to determine the brittleness index based on mineralogical data and thereafter be derived from seismic data.

Integrating Rock Physics, Seismic Reservoir Characterization and Static Modeling of Carbonates; a Case Study from the UAE

2010 ◽  
Author(s):  
Fathy El-Wazeer ◽  
Antonio Vizamora ◽  
Aysha Al Hamedi ◽  
Habeeba Al-Housani ◽  
Peter Abram ◽  
...  
Keyword(s):  
Reservoir Characterization ◽  
Rock Physics ◽  
Seismic Reservoir ◽  
Static Modeling

Application of Probabilistic Neural Network and Rock physics Analysis for Carbonate Reservoir Characterization: A Case Study from Onshore Supergiant Oil Field

2020 ◽  
Author(s):  
Ali Alali ◽  
Karl Stephen
Keyword(s):  
Neural Network ◽  
Elastic Properties ◽  
Acoustic Impedance ◽  
Reservoir Characterization ◽  
Probabilistic Neural Network ◽  
Rock Physics ◽  
Carbonate Reservoir ◽  
High Porosity ◽  
Tidal Channels ◽  
Porosity Distribution

<p>Identification and modeling of the carbonate tidal channels is key for finding sweet spots or areas at higher risk to water breakthroughs which have a significant impact on the development and monitoring of reservoir dynamic performance. However, such these channels cannot be easily characterize by conventional seismic attributes. It is important to decipher the complexity of carbonate tidal channel architecture with integrated multisource data and different approaches.</p><p>A step wise approach has been taken in this work. First, rock physics model was carried out to ensure that elastic properties can be applied for reservoir characterization from the seismic data. Then, post-stack seismic inversion was carried out on the high resolution of 3D seismic dataset. The seismically derived porosity estimation is undertaken using geostatistical method and multiattributes combination was used. Probabilistic neural network training technique was then performed to improve the results for thick reservoir and the result has been used for seismic conditioning of geological models. Finally, the spatial distribution of porosity volume was cautiously assessed through the comparison between input and blind wells, also validated by core data.</p><p>The analysis of rock physics displayed a high correlation between elastic properties and the porosity distribution of the Mishrif channel, three facies were observed. The final interpretation of seismically derived characterization in Mishrif channel, observed a different lateral distribution of inverted elastic properties. These features of Mishrif carbonate tidal channels could be classified into these regions: north, southwest, and east. Related a high porosity with low acoustic impedance appeared mostly in these channels which reflect a good reservoir quality grainstone channels or sholas bodies. While, outside these channels is heavily mud filled by peritidal carbonates and characterized a high acoustic impedance anomaly with low quality of porosity distribution.</p><p>The results provided a new insight into the distribution of the petrophysical properties and reservoir architecture of facies with quantification of their influence on dynamic reservoir behavior in the Mishrif channelized systems and also for similar heterogeneous carbonate reservoirs</p>

scholarly journals Integrated Well Data And 3D Seismic Inversion Study For Reservoir Delineation And Description

2020 ◽  
Vol 70 (1) ◽  
pp. 209-220
Author(s):  
Qazi Sohail Imran ◽  
◽  
Numair Ahmad Siddiqui ◽  
Abdul Halim Abdul Latif ◽  
Yasir Bashir ◽  
...  
Keyword(s):  
Reservoir Characterization ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Depositional Environments ◽  
Well Log ◽  
Reservoir Properties ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
And Performance ◽  
Well Log Analysis

Offshore petroleum systems are often very complex and subtle because of a variety of depositional environments. Characterizing a reservoir based on conventional seismic and well-log stratigraphic analysis in intricate settings often leads to uncertainties. Drilling risks, as well as associated subsurface uncertainties can be minimized by accurate reservoir delineation. Moreover, a forecast can also be made about production and performance of a reservoir. This study is aimed to design a workflow in reservoir characterization by integrating seismic inversion, petrophysics and rock physics tools. Firstly, to define litho facies, rock physics modeling was carried out through well log analysis separately for each facies. Next, the available subsurface information is incorporated in a Bayesian engine which outputs several simulations of elastic reservoir properties, as well as their probabilities that were used for post-inversion analysis. Vast areal coverage of seismic and sparse vertical well log data was integrated by geostatistical inversion to produce acoustic impedance realizations of high-resolution. Porosity models were built later using the 3D impedance model. Lastly, reservoir bodies were identified and cross plot analysis discriminated the lithology and fluid within the bodies successfully.

Facies — The drivers for modern inversions

2020 ◽  
Vol 39 (2) ◽  
pp. 102-109
Author(s):  
John Pendrel ◽  
Henk Schouten
Keyword(s):  
Bayesian Analysis ◽  
Prior Information ◽  
Reservoir Characterization ◽  
Low Frequency ◽  
Rock Physics ◽  
Well Log ◽  
Data Set ◽  
Prior Probabilities ◽  
Physics Model ◽  
Rock Physics Model

It is common practice to make facies estimations from the outcomes of seismic inversions and their derivatives. Bayesian analysis methods are a popular approach to this. Facies are important indicators of hydrocarbon deposition and geologic processes. They are critical to geoscientists and engineers. The application of Bayes’ rule maps prior probabilities to posterior probabilities when given new evidence from observations. Per-facies elastic probability density functions (ePDFs) are constructed from elastic-log and rock-physics model crossplots, over which inversion results are superimposed. The ePDFs are templates for Bayesian analysis. In the context of reservoir characterization, the new information comes from seismic inversions. The results are volumes of the probabilities of occurrences of each of the facies at all points in 3D space. The concepts of Bayesian inference have been applied to the task of building low-frequency models for seismic inversions without well-log interpolation. Both a constant structurally compliant elastic trend approach and a facies-driven method, where models are constructed from per-facies trends and initial facies estimates, have been tested. The workflows make use of complete 3D prior information and measure and account for biases and uncertainties in the inversions and prior information. Proper accounting for these types of effects ensures that rock-physics models and inversion data prepared for reservoir property analysis are consistent. The effectiveness of these workflows has been demonstrated by using a Gulf of Mexico data set. We have shown how facies estimates can be effectively used to build reasonable low-frequency models for inversion, which obviate the need for well-log interpolation and provide full 3D variability. The results are more accurate probability-based net-pay estimates that correspond better to geology. We evaluate the workflows by using several measures including precision, confidence, and probabilistic net pay.

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