Lithology and fluid prediction from seismic data and well log data

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.

Download Full-text

Identification of Natural Gas Hydrates in Blocks 25a, 25b, 26 and 27 Offshore Trinidad Using Available Well Log Data

10.2118/spe-169975-ms ◽

2014 ◽

Author(s):

M. M. Smith ◽

L. E. Sobers

Keyword(s):

Natural Gas ◽

Deep Water ◽

Gas Hydrates ◽

Seismic Data ◽

East Coast ◽

Depositional Environments ◽

Well Log ◽

Borehole Data ◽

Log Data ◽

Natural Gas Hydrates

Abstract Natural gas hydrates can be found in conventional hydrocarbon depositional environments such as clastic marine sediments, siltstones and unconsolidated sands and in oceanic environments for reservoir pressures greater than 663 psi (46 bars) and temperatures less than 20 °C. These conditions are found in the deep water (> 300 m) acreage off the South East coast of Trinidad. Natural gas hydrates have been recovered in this area during drilling and seismic data have shown that there may be deposits in some areas. In this study we reviewed all the available borehole data and employed well log interpretation techniques to identify natural gas hydrates in the deep water acreage blocks 25 a, 25 b, 26 and 27 off the Trinidad South East coast. The analysis of well log data for the given depths did not present evidence to suggest the presence of natural gas hydrates in Blocks 25 a, 25 b, 26 and 27. In this paper we present our analysis of the data available and recommend the formation depths which should be logged in during the deep water exploration drilling to confirm the seismic data and core data which indicate the presence of natural gas hydrates in these blocks.

Download Full-text

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

Solid Earth ◽

10.5194/se-7-943-2016 ◽

2016 ◽

Vol 7 (3) ◽

pp. 943-958 ◽

Cited By ~ 10

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.

Download Full-text

Broadband seismic amplitude variation with offset inversion

Geophysics ◽

10.1190/geo2016-0306.1 ◽

2017 ◽

Vol 82 (3) ◽

pp. M43-M53 ◽

Cited By ~ 9

Author(s):

Zhaoyun Zong ◽

Kun Li ◽

Xingyao Yin ◽

Ming Zhu ◽

Jiayuan Du ◽

...

Keyword(s):

Bayesian Inference ◽

Frequency Domain ◽

Seismic Data ◽

Low Frequency ◽

Well Log ◽

Complex Frequency ◽

Elastic Parameters ◽

Log Data ◽

Avo Inversion ◽

Frequency Components

Seismic amplitude variation with offset (AVO) inversion is well-known as a popular and pragmatic tool used for the prediction of elastic parameters in the geosciences. Low frequencies missing from conventional seismic data are conventionally recovered from other geophysical information, such as well-log data, for estimating the absolute rock properties, which results in biased inversion results in cases of complex heterogeneous geologic targets or plays with sparse well-log data, such as marine or deep stratum. Broadband seismic data bring new opportunities to estimate the low-frequency components of the elastic parameters without well-log data. We have developed a novel AVO inversion approach with the Bayesian inference for broadband seismic data. The low-frequency components of the elastic parameters are initially estimated with the proposed broadband AVO inversion approach with the Bayesian inference in the complex frequency domain because seismic inversion in the complex frequency domain is helpful to recover the long-wavelength structures of the elastic models. Gaussian and Cauchy probability distribution density functions are used for the likelihood function and the prior information of model parameters, respectively. The maximum a posteriori probability solution is resolved to estimate the low-frequency components of the elastic parameters in the complex frequency domain. Furthermore, with those low-frequency components as initial models and constraints, the conventional AVO inversion method with the Bayesian inference in the time domain is further implemented to estimate the final absolute elastic parameters. Synthetic and field data examples demonstrate that the proposed AVO inversion in the complex frequency domain is able to predict the low-frequency components of elastic parameters well, and that those low-frequency components set a good foundation for the final estimation of the absolute elastic parameters.

Download Full-text

Missing log data interpolation and semiautomatic seismic well ties using data matching techniques

Interpretation ◽

10.1190/int-2018-0044.1 ◽

2019 ◽

Vol 7 (2) ◽

pp. T347-T361 ◽

Cited By ~ 2

Author(s):

Sean Bader ◽

Xinming Wu ◽

Sergey Fomel

Keyword(s):

Seismic Data ◽

Local Similarity ◽

Well Log ◽

Data Set ◽

Log Data ◽

Data Matching ◽

Using Data ◽

Depth Relationship ◽

Matching Techniques ◽

3D Seismic Data

Relating well-log data, measured in depth, to seismic data, measured in time, typically requires estimating well-log impedance and a time-to-depth relationship using available sonic and density logs. When sonic and density logs are not available, it is challenging to incorporate wells into integrated reservoir studies because the wells cannot be tied to seismic. We have developed a workflow to estimate missing well-log information, automatically tie wells to seismic data, and generate a global well-log property volume using data matching techniques. We first used the local similarity scan to align all logs to constant geologic time and interpolate missing well-log information. Local similarity is then used to tie available wells with seismic data. Finally, log data from each well are interpolated along local seismic structures to generate global log property volumes. We use blind well tests to verify the accuracy of well-log interpolation and seismic well ties. Applying our workflow to a 3D seismic data set with 26 wells achieves consistent and verifiably accurate results.

Download Full-text

The Use of Well-Log Data in the Geomechanical Characterization of Middle Cambrian Tight Sandstone Formation: A Case Study from Eastern Pomerania, Poland

Energies ◽

10.3390/en14196022 ◽

2021 ◽

Vol 14 (19) ◽

pp. 6022

Author(s):

Małgorzata Słota-Valim ◽

Anita Lis-Śledziona

Keyword(s):

Seismic Data ◽

Reservoir Rock ◽

Rock Properties ◽

Hydraulic Fractures ◽

Well Log ◽

Tight Sandstone ◽

Middle Cambrian ◽

Log Data ◽

Geomechanical Characterization

Geomechanical characterization plays a key role in optimizing the stimulation treatment of tight reservoir formations. Petrophysical models help classify the reservoir rock as the conventional or unconventional type and determine hydrocarbon-saturated zones. Geomechanical and petrophysical models are fundamentally based on well-log data that provide reliable and high-resolution information, and are used to determine various relationships between measured borehole parameters and modeled physical rock properties in 3D space, with the support of seismic data. This paper presents the geomechanical characterization of the Middle Cambrian (Cm2) sediments from Eastern Pomerania, north Poland. To achieve the aim of this study, 1D well-log-based and 3D models based on seismic data of the rocks’ petrophysical, elastic, and strength properties, as well as numerical methods, were used. The analysis of the Middle Cambrian deposits revealed vertical and horizontal heterogeneity in brittleness, the direction of horizontal stresses, and the fracturing pressure required to initiate hydraulic fractures. The most prone to fracturing is the gas-saturated tight sandstones belonging to the Paradoxides Paradoxissimus formation of Cm2, exhibiting the highest brittleness and highest fracturing pressure necessary to stimulate this unconventional reservoir formation.

Download Full-text

Application of maximum likelihood and model-based seismic inversion techniques: a case study from K-G basin, India

Journal of Petroleum Exploration and Production Technology ◽

10.1007/s13202-021-01401-0 ◽

2021 ◽

Author(s):

Richa ◽

S. P. Maurya ◽

Kumar H. Singh ◽

Raghav Singh ◽

Rohtash Kumar ◽

...

Keyword(s):

Maximum Likelihood ◽

Seismic Data ◽

Acoustic Impedance ◽

Gamma Ray ◽

Seismic Inversion ◽

Reservoir Rock ◽

Well Log ◽

Log Data ◽

Petrophysical Parameters ◽

Model Based

AbstractSeismic inversion is a geophysical technique used to estimate subsurface rock properties from seismic reflection data. Seismic data has band-limited nature and contains generally 10–80 Hz frequency hence seismic inversion combines well log information along with seismic data to extract high-resolution subsurface acoustic impedance which contains low as well as high frequencies. This rock property is used to extract qualitative as well as quantitative information of subsurface that can be analyzed to enhance geological as well as geophysical interpretation. The interpretations of extracted properties are more meaningful and provide more detailed information of the subsurface as compared to the traditional seismic data interpretation. The present study focused on the analysis of well log data as well as seismic data of the KG basin to find the prospective zone. Petrophysical parameters such as effective porosity, water saturation, hydrocarbon saturation, and several other parameters were calculated using the available well log data. Low Gamma-ray value, high resistivity, and cross-over between neutron and density logs indicated the presence of gas-bearing zones in the KG basin. Three main hydrocarbon-bearing zones are identified with an average Gamma-ray value of 50 API units at the depth range of (1918–1960 m), 58 API units (2116–2136 m), and 66 API units (2221–2245 m). The average resistivity is found to be 17 Ohm-m, 10 Ohm-m, and 12 Ohm-m and average porosity is 15%, 15%, and 14% of zone 1, zone 2, and zone 3 respectively. The analysis of petrophysical parameters and different cross-plots showed that the reservoir rock is of sandstone with shale as a seal rock. On the other hand, two types of seismic inversion namely Maximum Likelihood and Model-based seismic inversion are used to estimate subsurface acoustic impedance. The inverted section is interpreted as two anomalous zones with very low impedance ranging from 1800 m/s*g/cc to 6000 m/s*g/cc which is quite low and indicates the presence of loose formation.

Download Full-text

Brittleness index calculation based on amplitude-variation-with-offset inversion for coal-bed methane reservoir: A case study of the Qinshui Basin, China

Interpretation ◽

10.1190/int-2019-0048.1 ◽

2020 ◽

Vol 8 (1) ◽

pp. SA63-SA72

Author(s):

Wu Haibo ◽

Cheng Yan ◽

Zhang Pingsong ◽

Dong Shouhua ◽

Huang Yaping

Keyword(s):

Seismic Data ◽

Brittleness Index ◽

Coal Bed ◽

Coal Bed Methane ◽

Well Log ◽

Amplitude Variation ◽

Frequency Model ◽

Log Data ◽

Amplitude Variation With Offset ◽

Avo Inversion

The brittleness index (BI) is an important parameter for coal-bed methane (CBM) reservoir fracturing characterization. Most published studies have relied on petrophysical and well-log data to estimate the geomechanical properties of reservoir rocks. The major drawback of such methods is the lack of control away from well locations. Therefore, we have developed a method of combining BI calculation from well logs with that inverted from 3D seismic data to overcome the limitation. A real example is given here to indicate the workflow. A traditional amplitude-variation-with-offset (AVO) inversion was conducted first. BI for the CBM reservoir was then calculated from the Lamé constants inverted from prestack seismic data through a traditional AVO inversion method. We build an initial low-frequency model based on the well-log data. Comparison of the seismic inverted BI at the target reservoir and BI extracted from the well-log data showed satisfactory results. This method has been proved to be efficient and effective enough at identifying BI sweet spots in CBM reservoirs.

Download Full-text