Advanced geophysical sensing while drilling is being driven by trends to automate and optimize drilling and the desire to better characterize complex near surface and overburden in desert environments. We introduce the DrillCAM system, which combines a set of geophysical techniques from seismic while drilling (SWD), drill-string vibration health, estimation of formation properties at the bit, and imaging ahead of and around the bit. We present data acquisition, processing, and initial application results from the first field trial on an onshore well in a desert environment. In this study, we focus on SWD applications. For the first time, wireless geophones installed around a rig were used to acquire continuous data while drilling. We demonstrate the feasibility of such a system to provide flexible acquisition geometries that are easily expandable with increasing bit depth without interference from drilling operations. Using a top-drive sensor as a pilot, we transform the drill-bit noise into meaningful and reliable seismic signals. The data were used to retrieve a check shot while drilling, make kinematic look-ahead predictions, and obtain a vertical seismic profiling corridor stack matching surface seismic. Robust near-offset check-shot signals were received from roller-cone and polycrystalline diamond compact (PDC) bits above 7200 ft after limited preprocessing of challenging single-sensor data with supergrouping. Detecting signals from deeper sections drilled with PDC bits may require more advanced processing by using an entire 2D spread of wireless geophones and downhole pilots. The real-time capabilities of the system make the data available for continuous data processing and interpretation that will facilitate drilling automation and improve real-time decision making.

We present results from a first of its kind permanent seismic monitoring system in a desert environment. This new system consists of sensors buried at 70 m depth and surface vibroseis sources. We describe processing challenges associated with this single-sensor and single-source dataset and present initial solutions that allowed us to obtain robust 3D reservoir images. The system has achieved a remarkable repeatability with mean NRMS of 4-5 % across high fold area between closely spaced repeat surveys. Presentation Date: Wednesday, October 19, 2016 Start Time: 8:50:00 AM Location: 146 Presentation Type: ORAL

DrillCAM aims to deliver continuous geophysical measurements while drilling to enable drilling optimization and automation. The first field trial was conducted in an onshore well and delivered a complete dataset from surface to 10,000 ft. Three datasets were continuously recorded including from 1) a large array of surface wireless geophones, 2) accelerometers placed on the top drive and 3) a downhole vibration tool near the drill bit. Novel wireless geophones delivered seismic data close to real time during drilling and enabled flexible adaptive geometries with depth. An innovative continuously recording downhole tool comprising a sub-millisecond sampled accelerometer acted as an invaluable stethoscope at the rockface next to the bit. Data analysis is ongoing to harvest the value of this unique data for drilling optimization. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 1:50 PM Presentation Time: 1:50 PM Location: 221A Presentation Type: Oral

Seismic monitoring of an onshore carbonate reservoir in a desert environment has been achieved for the first time. Optimizing data repeatability was key to detecting the weak 4D (time-lapse) signal resulting from a fluid-injection program, which was achieved through a combination of specialized survey design, careful acquisition, and dedicated 4D processing. The hybrid acquisition system utilized buried geophones, which significantly reduced 4D noise caused by variability in the near-surface environment. Despite the extensive acquisition efforts, time-lapse processing is an essential component of achieving highly repeatable data. A fit-for-purpose workflow was developed to reduce the remaining 4D noise using a combination of parallel and simultaneous processing. Processing steps leading to the largest improvement in reflection signal-to-noise ratio, such as noise attenuation, amplitude balancing, and supergrouping, produced the largest reduction in 4D noise. Outstanding final migrated data repeatability has been achieved, comparable to levels reported for the more favorable permanent marine systems. However, the need to use surface sources results in a seasonal imprint on data repeatability, which hinders the interpretation of surveys acquired during different seasons. In the absence of a fully buried acquisition system, advanced processing techniques such as surface-consistent matching filters may be required to resolve these variations.

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.

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.