Emad Hemyari

This article presents an advanced processing workflow for a Seismic While Drilling (SWD) dataset acquired using Distributed Acoustic Sensing (DAS) in a cross-well setting at the Otway International Test Centre (OITC) in Victoria, Australia, where no pilot signals were recorded. Recording the drill bit signature enables and simplifies the decoding of passive seismic signals emitted by the drill bit while drilling. In conventional SWD, a measured drill bit signature is used to correlate passive seismic recordings and to determine source trigger times, analogous to vibroseis processing. Without this reference, both source timing and signature must be inferred from the recorded wavefield. This can typically be achieved by backpropagating the recorded seismic data over short time windows, estimating the source location and trigger time based on the peak RMS energy in space and time. However, to simplify the processing of SWD data, a data processing workflow is presented, guided by travel time and seismic modelling, which transforms passive SWD data into active equivalents. The transformed data can then be used to characterize the subsurface by implementing travel time tomography and cross-well imaging. The results demonstrate reliable velocity and structural information can be recovered from DAS-based SWD data without pilot measurements, enabling simplified and scalable deployment of passive seismic while-drilling workflows.

Passive seismic surveys have attracted interest for use in many geological and geotechnical applications in the past few decades, mainly in reconstructing models of near-surface properties. They are also of interest in the mineral exploration of shallow deposits where targets lay on or within the bedrock and are covered by loose sediments above. The goal of this article was to test the effectiveness of cheap methods to understand the cover thickness and its lateral variations, which is essential to map the top of the bedrock. We investigated the use of passive seismic surveys to retrieve Rayleigh surface waves and invert them by analyzing their dispersion to reconstruct near-surface shear-wave velocity profiles. Using readily available passive seismic sources is advantageous compared to using costly active sources. Passive seismic data acquired by geophones and DAS showed the potential and challenges of using different sensing technologies. We demonstrated an approach combining passive seismic interferometry and multichannel analysis of surface waves (MASW). Computed dispersion images from both geophone and DAS data provided an improved understanding of their usability for subsurface model building and factors affecting their quality. Some of these factors are related to the surrounding environment, present noise sources, acquisition setup, and the methods used in reconstructing the dispersion images and inverting them. Successful demonstration of MASW was achieved with a relatively short period of continuous recording using a 2D array of geophones at a mineral exploration site in the Pilbara region of Western Australia.

Exploring shallow mineral resources requires acquiring denser seismic data, for which Distributed Acoustic Sensing is an effective enabler and relevant to mining operations monitoring. Passive seismic can be of interest in characterizing the subsurface; however, dealing with large amounts of data pushes against the limits of existing computational systems and algorithms, especially for continuous monitoring. Hence, more than ever, novel data analysis methods are needed. In this article, we investigate using synthetic seismic data, paired with real noise recordings, as part of a supervised deep-learning neural network methodology to detect and locate induced seismic sources and explore their potential use to reconstruct subsurface properties. Challenges of this methodology were identified and addressed in the context of induced seismicity applications. Data acquisition and modelling were discussed, preparation workflows were implemented, and the method was demonstrated on synthetic data and tested on relevant seismic monitoring field dataset from the Otway CO₂ injection site. Conducted tests confirmed the effects of time shifts, signal-to-noise ratios, and geometry mismatches on the performance of trained models. Those promising results showed the method’s applicability and paved the way for potential application to more field data, such as seismic while drilling.

Processing seismic data from drillbit-generated vibrations requires a reliable source signature for correlation and deconvolution purposes. Recently, a land field trial has been conducted in a desert environment. A memory-based downhole vibration accelerometer has been used together with a more conventional top-drive sensor to continuously record the pilot signal from 590 to 8600 ft (180–2621 m). Past results indicate that seismic-while-drilling (SWD) data processed using the top-drive accelerometer exhibit good quality in the middle sections of the well but a reduced signal-to-noise ratio for shallow and deep sections. One of the main challenges in using the downhole pilot is a substantial drift of the downhole clock time. To resolve it, a novel automated time-alignment procedure using the GPS-synchronized signal of the top-drive sensor as a reference is applied. The downhole recording provides a source signature of better quality. In shallow sections of the well, it helps to overcome the intense surface-related vibrational noise, whereas, in deeper sections, it provides a cleaner extraction of weaker signals from the polycrystalline diamond compact bits. Processing with the downhole pilot results in better surface seismic data quality than with a conventional top-drive sensor. Therefore, enabling the use of the synchronized downhole pilot signal is of crucial importance for SWD applications. Modern cost-effective near-bit vibrational sensors widely used for different nonseismic applications could be an effective acquisition solution, as shown in this study.

We present processing details of seismic-while-drilling data recently acquired on one of the onshore wells by a prototype DrillCAM system with wireless geophones, top-drive, and downhole vibration sensors. The general flow follows an established practice and consists of correlation with a drillbit pilot signal, vertical stacking, and pilot deconvolution. This work's novelty is the usage of the memory-based near-bit sensor with a significant time drift reaching 30-40 minutes at the end of each drilling run. A data-driven automatic time alignment procedure is developed to accurately eliminate time drift error by utilizing the top-drive acceleration sensor as a reference. After the alignment, the processing flow can utilize the top-drive or the near-bit pilots similarly. We show each processing step's effect on the final data quality and discuss some implementation details.

When a drillbit is used as a source, processing seismic-while-drilling (SWD) data requires a recorded pilot signal as the source signature. We present initial results from a field trial where a memory-based near-bit vibration sensor is used together with a more conventional top-drive sensor. Drift in downhole clock time is resolved by a novel automated time-alignment procedure using the GPS-synchronized top-drive sensor as a reference. The downhole sensor records a pilot of better fidelity in shallow sections of the well subject to intense vibrational noise and deep sections drilled with polycrystalline diamond compact (PDC) bits. We demonstrate that better quality seismic data is achieved using a downhole pilot compared to a more traditional surface pilot from the top-drive sensor.

Advanced seismic-while-drilling (SWD) technologies are being utilized to steer drilling operations and provide high-resolution subsurface images around and ahead of the bit. We present a case study of SWD imaging using a recently acquired field data set from a desert environment with a complex near surface. Data acquisition is performed with wireless geophones and top-drive sensors using continuous real-time recording. The drill-bit noise data are analyzed while continuously recording in real time by using a specialized workflow that combines elements of SWD and conventional vertical seismic profiling processing with controlled seismic sources. First, the workflow enhances the direct wavefield to retrieve accurate first-break picks for traveltime tomographic inversion along east–west- and north–south-striking walkaway lines. Then, it extracts and enhances upgoing reflection events, illuminating parts of the subsurface around and ahead of the bit. During the final step, these upgoing reflections are imaged using the inverted velocity model to reconstruct a migrated subsurface image around the well. As is the case for land surface seismic in the presence of a complex near surface, we observe a significant variation of data quality for the orthogonal receiver lines. As a result, each line provides a robust image of a different part of the subsurface. The east–west-striking line's migrated image delineates a major shallow reflector that serves as a marker for predicting the drilling depth of a deeper horizon. Likewise, migrating upgoing reflections from the north–south line accurately maps a deeper target horizon ahead of the bit. The obtained SWD images assist in setting the casing points accurately and provide a more precise ahead-of-the-bit depth for different horizons with significantly less uncertainty than surface seismic.