The Value of Seismic Data in Conventional and Unconventional Fields

The paper illustrates the value of seismic data in different environments after assessing the benefits and costs of processes such as seismic acquisition, seismic processing and seismic interpretation. Global examples from conventional and unconventional fields are discussed to show how seismic data plays a significant role in determining low-risk and high-reward wells and also eliminating the high-risk and low-reward wells. This paper shows an example of a conventional field in the state of Kansas, USA, where the net present value (NPV) increased by more than 17 times when 3D seismic data was acquired, while in an unconventional field the commercial success rate rose from 30% to 70% due to 3D seismic acquisition. However, two offshore fields in the Republic of Trinidad and Tobago are discussed to show that the NPV as impacted by advanced seismic processing was more than 111 ($M). Another example comes from Viking Field, a conventional field in Canada, where the NPV was increased from 3800 ($M) to 5000 ($M) when the seismic data was re-processed. Furthermore, the value of investing in seismic data was investigated and quantified by comparing two synthetically modeled scenarios in Saudi Arabia. Overall, the four examples from North America, Central America and Saudi Arabia illustrate that investment in seismic data has a positive impact on both conventional and unconventional fields. That provides strong evidence to encourage more investments in geophysical technologies.

Workshop Review: Recent workshop explored seismic processing advances for reservoir characterization

The first Seismic Processing Advances for Reservoir Characterization Workshop was held 29 to 31 March 2021. Maximizing the value of existing fields has become more important than ever, and proper reservoir characterization is essential to make sound business decisions. While well and production data are valuable sources of information, seismic is the key tool that can help us understand what is happening between wells.

Acquisition/Processing: Machine learning-based deblending: Dispersed source array data example

Machine learning (ML) has proven its value in the seismic industry with successful implementations in areas of seismic interpretation such as fault and salt dome detection and velocity picking. The field of seismic processing research also is shifting toward ML applications in areas such as tomography, demultiple, and interpolation. Here, a supervised ML deblending algorithm is illustrated on a dispersed source array (DSA) data example in which both high- and low-frequency vibrators were deployed simultaneously. Training data pairs of blended and corresponding unblended data were constructed from conventional (unblended) data from another survey. From this training data, the method can automatically learn a deblending operator that is used to deblend for both the low- and the high-frequency vibrators of the DSA data. The results obtained on the DSA data are encouraging and show that the ML deblending method can offer a good performing, less user-intensive alternative to existing deblending methods.

Non-stationary local signal-and-noise orthogonalization

The local signal-and-noise orthogonalization method has been widely used in the seismic processing and imaging community. In the local signal-and-noise orthogonalization method, a fixed triangle smoother is used for regularizing the local orthogonalization weight, which is based on the assumption that the energy is homogeneously distributed across the whole seismic profile. The fixed triangle smoother limits the performance of the local orthogonalization method in processing complicated seismic datasets. Here, we propose a new local orthogonalization method that uses a variable triangle smoother. The non-stationary smoothing radius is obtained by solving an optimization problem, where the low-pass filtered seismic data are matched by the smoothed data in terms of the local frequency attribute. The new local orthogonalization method with non-stationary model smoothness constraint is called the non-stationary local orthogonalization method. We use several synthetic and field data examples to demonstrate the successful performance of the new method.

Supplemental Material: Early and middle Miocene ice sheet dynamics in the Ross Sea: Results from integrated core-log-seismic interpretation

Additional information regarding methods (Reflection seismic processing, Drill-site measurements, Core-log-seismic correlations, Spatial Velocity calculations, and Reflection Tomography model) and regional stratigraphy descriptions, as well as detailed considerations regarding the opal distribution and depth.

Supplemental Material: Early and middle Miocene ice sheet dynamics in the Ross Sea: Results from integrated core-log-seismic interpretation

Additional information regarding methods (Reflection seismic processing, Drill-site measurements, Core-log-seismic correlations, Spatial Velocity calculations, and Reflection Tomography model) and regional stratigraphy descriptions, as well as detailed considerations regarding the opal distribution and depth.