A commercial marine seismic survey has been completed with the wavefield from the n‐element (single guns and clusters) airgun array measured for every shot using an array of n + 2 near‐field hydrophones, n of which were required to determine the source wavefield, the remaining two providing a check on the computation. The source wavefield is critical to the determination of the seismic wavelet for the extraction of reflection coefficients from seismic reflection data and for tying the data to wells. The wavefield generated by the full array of interacting airguns can be considered to be the superposition of n spherical pressure waves, or notional source signatures, the n hydrophone measurements providing a set of n simultaneous equations for each shot. The solution of the equations for the notional source signatures requires three ingredients: the geometry of the gun ports and near‐field hydrophones; the sensitivity of each hydrophone recording channel; and the relative motion between the near‐field hydrophones and the bubbles emitted by the guns. The geometry was measured on the back deck using a tape measure. A calibration data set was obtained at the approach to each line, in which each gun was fired on its own and the resulting wavefield was measured with the near‐field hydrophones and recorded. The channel sensitivities, or conversion from pressure at the hydrophones to numbers on the tape, were found for each near‐field hydrophone channel using the single gun calibration data, the measured geometry, and the peak pressure from each gun, known from the manufacturer’s calibration. The relative motion between the guns and hydrophones was obtained from the same calibration data set by minimizing the energy in the computed notional source signatures at the guns which did not fire. The full array data were then solved for the notional source signatures, and the pressure was computed at the two spare hydrophones and compared with the actual recordings. The rms errors were 5.3% and 2.8% and would have been smaller if the hydrophone channel sensitivities had been properly calibrated beforehand and if the movement of the guns with respect to the hydrophones had been more restricted. This comparison of the predicted and measured signatures at spare hydrophones can, in principle, be done on every shot and we recommend that this be implemented as a standard quality control procedure whenever it is desired to measure the wavefield of a marine seismic source.
Three-dimensional (3D) seismic data have had a substantial impact on the successful exploration and production of hydrocarbons. Although most commonly acquired by the oil and gas exploration industry, these data are starting to be used as a research tool in other Earth sciences disciplines. However despite some innovative new directions of academic investigation, most of the examples of how 3D seismic data have increased our understanding of the structure and stratigraphy of sedimentary basins come from the industry that acquired these data. The 3D seismic tool is also making significant inroads into other areas of Earth sciences, such as igneous and structural geology. However, there are pitfalls that parallel these advances: geoscientists need to be multidisciplined and true integrators, and at the same time have an ever-increasing knowledge of geophysical acquisition and processing. Notably the utility of the 3D seismic tool seems to have been overlooked by most of the academic community, and we would submit that academia has yet to take full advantage of this technology as a research tool. We propose that the remaining scientific potential far exceeds the advances made thus far and major opportunities, as well as challenges, lie ahead. DAVIES, R.
We examine the conventional methodology for tying wells to processed seismic data and show why this methodology fails to allow for reliable interpretation of the seismic data for stratigraphy. We demonstrate an alternative methodology that makes the tie without the use of synthetic seismograms, but at the price of measuring the seismic source signature, the cost of such measurements being about 1% of data acquisition costs. The essence of the well tie is (1) to identify geological and seismic interfaces from the logs and core, (2) to measure the one‐way traveltime to these interfaces using downhole geophones, and (3) to use the polarity information from (1) and the timing information from (2) to identify the horizons on the zero‐phase processed seismic data. Conventional processing of seismic data usually causes the wavelet to vary from trace to trace, and conventional wavelet extraction at a well using the normal‐incidence reflection coefficients relies on a convolutional relationship between these coefficients and the processed data that has no basis in the physics of the problem. Each new well introduces a new wavelet and poses a new problem—how should the zero‐phasing filter be derived between wells? Our methodology consists of three steps: (1) determination of the wavelet consisting of all known convolutional effects before any processing using measurements of the source time function made during data acquisition, (2) compression of this wavelet to the shortest zero‐phase wavelet within the bandwidth available, and (3) elimination of uncontrolled distortions to the wavelet in subsequent processing. This method is illustrated with data from the prospective Jurassic succession in the Moray Firth rift arm of the North Sea in which we have identified, for the first time on seismic data, a major regional unconconformity that cuts out more than 20 Ma of geological time. This method offers two major benefits over the conventional approach. First, all lateral variations in the processed seismic data are caused by the geology. Second, events on the processed seismic data may be identified from well logs simply by their polarity and timing. It follows that events can then be followed on the seismic data from one well to another with confidence, the seismic data can be interpreted for stratigraphy, and subtle stratigraphic traps may be identified.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.