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.

Seismic reservoir characterization of Bone Spring and Wolfcamp Formations in the Delaware Basin — A case study: Part 1

2020 ◽  
Vol 8 (4) ◽  
pp. T927-T940
Author(s):  
Satinder Chopra ◽  
Ritesh Kumar Sharma ◽  
James Keay
Keyword(s):  
Reservoir Characterization ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Delaware Basin ◽  
Seismic Reservoir ◽  
Data Conditioning ◽  
Inversion Analysis ◽  
Sweet Spots

The Delaware and Midland Basins are multistacked plays with production being drawn from different zones. Of the various prospective zones in the Delaware Basin, the Bone Spring and Wolfcamp Formations are the most productive and thus are the most drilled zones. To understand the reservoirs of interest and identify the hydrocarbon sweet spots, a 3D seismic inversion project was undertaken in the northern part of the Delaware Basin in 2018. We have examined the reservoir characterization exercise for this dataset in two parts. In addition to a brief description of the geology, we evaluate the challenges faced in performing seismic inversion for characterizing multistacked plays. The key elements that lend confidence in seismic inversion and the quantitative predictions made therefrom are well-to-seismic ties, proper data conditioning, robust initial models, and adequate parameterization of inversion analysis. We examine the limitations of a conventional approach associated with these individual steps and determine how to overcome them. Later work will first elaborate on the uncertainties associated with input parameters required for executing rock-physics analysis and then evaluate the proposed robust statistical approach for defining the different lithofacies.

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.

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.

Seismic reservoir characterization of a U.S. Midcontinent fluvial system using rock physics, poststack seismic attributes, and neural networks

2002 ◽  
Vol 21 (5) ◽  
pp. 428-436 ◽  
Author(s):  
Joel D. Walls ◽  
M. Turhan Taner ◽  
Gareth Taylor ◽  
Maggie Smith ◽  
Matthew Carr ◽  
...  
Keyword(s):  
Neural Networks ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Seismic Attributes ◽  
Fluvial System ◽  
Seismic Reservoir

scholarly journals Joint interpretation of magnetotelluric, seismic, and well-log data in Hontomín (Spain)

Solid Earth ◽  
2016 ◽  
Vol 7 (3) ◽  
pp. 943-958 ◽  
Author(s):  
Xènia Ogaya ◽  
Juan Alcalde ◽  
Ignacio Marzán ◽  
Juanjo Ledo ◽  
Pilar Queralt ◽  
...  
Keyword(s):  
Seismic Data ◽  
Velocity Model ◽  
Unconsolidated Sediments ◽  
Well Log ◽  
Storage Site ◽  
Near Surface ◽  
Log Data ◽  
Velocity Models ◽  
Joint Interpretation

Abstract. Hontomín (N of Spain) hosts the first Spanish CO2 storage pilot plant. The subsurface characterization of the site included the acquisition of a 3-D seismic reflection and a circumscribed 3-D magnetotelluric (MT) survey. This paper addresses the combination of the seismic and MT results, together with the available well-log data, in order to achieve a better characterization of the Hontomín subsurface. We compare the structural model obtained from the interpretation of the seismic data with the geoelectrical model resulting from the MT data. The models correlate well in the surroundings of the CO2 injection area with the major structural differences observed related to the presence of faults. The combination of the two methods allowed a more detailed characterization of the faults, defining their geometry, and fluid flow characteristics, which are key for the risk assessment of the storage site. Moreover, we use the well-log data of the existing wells to derive resistivity–velocity relationships for the subsurface and compute a 3-D velocity model of the site using the 3-D resistivity model as a reference. The derived velocity model is compared to both the predicted and logged velocity in the injection and monitoring wells, for an overall assessment of the computed resistivity–velocity relationships. The major differences observed are explained by the different resolution of the compared geophysical methods. Finally, the derived velocity model for the near surface is compared with the velocity model used for the static corrections in the seismic data. The results allowed extracting information about the characteristics of the shallow unconsolidated sediments, suggesting possible clay and water content variations. The good correlation of the velocity models derived from the resistivity–velocity relationships and the well-log data demonstrate the potential of the combination of the two methods for characterizing the subsurface, in terms of its physical properties (velocity, resistivity) and structural/reservoir characteristics. This work explores the compatibility of the seismic and magnetotelluric methods across scales highlighting the importance of joint interpretation in near surface and reservoir characterization.

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