Deep Thinking: 4C Proves Value on Seafloor

Marine 4C seismic technology was developed to assist hydrocarbon exploration and development – but 4C data have important marine engineering applications that have not been exploited.

The data discussed here illustrate how 4C data can be used to define geomechanical properties of a seafloor where engineers need to install production facilities.

Emphasis is placed here on determining bulk moduli and shear moduli of seafloor sediment. Bulk modulus, K, for a homogeneous medium is given by the equation:

K = [(VP)2 – (4/3)(VS)2]

Shear modulus, μ, for the same homogeneous material is defined by:

μ = (VS)2.

In these expressions, VP and VS are, respectively, P-wave and S-wave velocities in seafloor sediment, and is the bulk density of a sediment sample.

Figure 1 presents shallow data windows of compressional (P-P) and converted-shear (P-SV) profiles across an area of 4C/3D data acquisition. Data analysis will be confined to the layer extending from the seafloor (labeled WB) to horizon H4 shown on the profiles.

Procedures used by the seismic data processor caused the water bottom interface WB to not be imaged on the P-SV profile.

The profile crosses a gas-invaded zone centered on crossline coordinate 200. P-P horizons H1 through H4 are interpreted to be depth-equivalent surface to P-SV horizons H1 through H4.

For simplicity, the bulk density term in the two equations above is assumed to have a constant value of 1.8 gm/cm3 across the data analysis space.

Figure 2 displays seismic-derived VP velocities and calculated bulk moduli across the shallowest seafloor layer (WB to H4), and seismic-derived VS velocities and shear moduli values calculated for the layer are shown on figure 3.

Each elastic constant is shown as a 3-D surface and also in plan view. The position of the example profile (figure 1) is marked across each 3-D surface and illustrates the relationship between the gas-invaded zone seen on the P-P image and a normal fault that extends across much of the image area in the vicinity of crossline coordinate 200.

These figures show there is a one-to-one relationship between VP and bulk modulus, and between VS and shear modulus, for these high-porosity, near-seafloor, unconsolidated sediments.

Referring to equation 2, it is no surprise that VS and μ have a one-to-one correlation. The one-to-one relationship between VP and K is caused by the fact VP is much larger than VS within this shallowest seafloor layer.

In areas having hard seafloor sediment and for deeper layers where the VP/VS ratio has values appropriate for consolidated rocks, the VS term of equation 1 will be significant, and there will not be such a close correlation between K and VP.

The multicomponent seismic data application illustrated by this example can be done more rigorously by implementing a data-point by data-point inversion to create thin VP and VS layers that provide greater detail about zones of mechanical weakness.

The intent of this example is only to document that even simple velocity analyses of 4C data allow weak and strong areas to be recognized across a seafloor.

Of the two elastic moduli that are considered, shear modulus is particularly important for understanding where seafloor slumping is likely to occur.

Without 4C data, it is difficult to estimate shear moduli across large seafloor areas and to identify areas where seafloor slumping may be expected.

Comments (0)


Image Gallery

See Also: Book

Desktop /Portals/0/PackFlashItemImages/WebReady/book-m102-Electron-Microscopy-of-Shale-Hydrocarbon-Reservoirs.jpg?width=50&h=50&mode=crop&anchor=middlecenter&quality=90amp;encoder=freeimage&progressive=true 4072 Book

See Also: Bulletin Article

The influence of moisture, temperature, coal rank, and differential enthalpy on the methane (CH4) and carbon dioxide (CO2) sorption capacity of coals of different rank has been investigated by using high-pressure sorption isotherms at 303, 318, and 333 K (CH4) and 318, 333, and 348 K (CO2), respectively. The variation of sorption capacity was studied as a function of burial depth of coal seams using the corresponding Langmuir parameters in combination with a geothermal gradient of 0.03 K/m and a normal hydrostatic pressure gradient. Taking the gas content corresponding to 100% gas saturation at maximum burial depth as a reference value, the theoretical CH4 saturation after the uplift of the coal seam was computed as a function of depth. According to these calculations, the change in sorption capacity caused by changing pressure, temperature conditions during uplift will lead consistently to high saturation values. Therefore, the commonly observed undersaturation of coal seams is most likely related to dismigration (losses into adjacent formations and atmosphere). Finally, we attempt to identify sweet spots for CO2-enhanced coalbed methane (CO2-ECBM) production. The CO2-ECBM is expected to become less effective with increasing depth because the CO2-to-CH4 sorption capacity ratio decreases with increasing temperature and pressure. Furthermore, CO2-ECBM efficiency will decrease with increasing maturity because of the highest sorption capacity ratio and affinity difference between CO2 and CH4 for low mature coals.

Desktop /Portals/0/PackFlashItemImages/WebReady/Bulletin-cover-Feb-14-400px.jpg?width=50&h=50&mode=crop&anchor=middlecenter&quality=90amp;encoder=freeimage&progressive=true 5777 Bulletin Article

The Marcellus Shale is considered to be the largest unconventional shale-gas resource in the United States. Two critical factors for unconventional shale reservoirs are the response of a unit to hydraulic fracture stimulation and gas content. The fracture attributes reflect the geomechanical properties of the rocks, which are partly related to rock mineralogy. The natural gas content of a shale reservoir rock is strongly linked to organic matter content, measured by total organic carbon (TOC). A mudstone lithofacies is a vertically and laterally continuous zone with similar mineral composition, rock geomechanical properties, and TOC content. Core, log, and seismic data were used to build a three-dimensional (3-D) mudrock lithofacies model from core to wells and, finally, to regional scale. An artificial neural network was used for lithofacies prediction. Eight petrophysical parameters derived from conventional logs were determined as critical inputs. Advanced logs, such as pulsed neutron spectroscopy, with log-determined mineral composition and TOC data were used to improve and confirm the quantitative relationship between conventional logs and lithofacies. Sequential indicator simulation performed well for 3-D modeling of Marcellus Shale lithofacies. The interplay of dilution by terrigenous detritus, organic matter productivity, and organic matter preservation and decomposition affected the distribution of Marcellus Shale lithofacies distribution, which may be attributed to water depth and the distance to shoreline. The trend of normalized average gas production rate from horizontal wells supported our approach to modeling Marcellus Shale lithofacies. The proposed 3-D modeling approach may be helpful for optimizing the design of horizontal well trajectories and hydraulic fracture stimulation strategies.

Desktop /Portals/0/PackFlashItemImages/WebReady/organic-rich-marcellus-shale-lithofacies-modeling.jpg?width=50&h=50&mode=crop&anchor=middlecenter&quality=90amp;encoder=freeimage&progressive=true 5725 Bulletin Article
The fact that velocity models based on seismic reflection surveys commonly do not consider the near-surface geology necessitates filling the gap between the top of a velocity model and the surface of the Earth. In this study, we present a new workflow to build a shallow geologic model based exclusively on borehole data and corroborated by laboratory measurements. The study area is in Chemery (France), located at the southwestern border of the Paris Basin, where a large amount of borehole data is publicly available. The workflow starts with identifying lithologic interfaces in the boreholes and interpolating them between the boreholes. The three-dimensional (3-D) geometry of the lithologies then allows interpretation of the position, orientation, and offset of fault planes. Given the importance of the fault interpretation in the modeling process, a combination of different approaches is used to obtain the most reasonable structural framework. After creating a 3-D grid, the resulting 3-D structural model is populated with upscaled velocity logs from the boreholes, yielding the final near-surface P-wave velocity model. To better constrain the velocity model, we conducted laboratory measurements of P- and S-wave velocities in dry and water-saturated conditions on all lithologies in the model. The laboratory data were used to populate the 3-D near-surface model with VP/VS ratio values. The presented workflow accounts for one-dimensional borehole data and is much more iterative and time-consuming than workflows based on two-dimensional seismic sections. Nevertheless, the workflow results in a robust 3-D near-surface model allowing for structural interpretations and revealing the 3-D seismic velocity field.
Desktop /Portals/0/PackFlashItemImages/WebReady/building-a-three-dimensional-near-surface-geologic.jpg?width=50&h=50&mode=crop&anchor=middlecenter&quality=90amp;encoder=freeimage&progressive=true 5684 Bulletin Article

See Also: CD DVD

Desktop /Portals/0/images/_site/AAPG-newlogo-vertical-morepadding.jpg?width=50&h=50&mode=crop&anchor=middlecenter&quality=90amp;encoder=freeimage&progressive=true 4332 CD-DVD