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.

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.

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Saudi Aramco recently started the company's first CO2-EOR demonstration project in an onshore carbonate reservoir. Time-lapse (4D) seismic has proven to be a valuable reservoir management tool for monitoring the areal expansion of CO2 plumes in many similar projects around the world. However, the complex and dynamic nature of the near surface encountered in the desert environments of the Middle East results in high levels of 4D noise. This noise, coupled with the weak 4D signal expected from injection into a stiff carbonate reservoir, makes mapping the time lapse signal very challenging. The objective of this project was to develop a highly repeatable system capable of detecting small reservoir changes related to CO2 injection to enable the plume expansion to be tracked over time. Achieving highly repeatable seismic data requires specialized seismic acquisition and dedicated processing. A novel acquisition system using buried receivers was adopted to reduce 4D noise resulting from near-surface variations. To minimize the non-repeatability inherent in using surface sources, a differential GPS guidance system was implemented to ensure high positioning accuracy. Even with these acquisition efforts, a fit-for-purpose 4D processing workflow was necessary to further reduce differences between surveys. Despite the challenges faced, outstanding data repeatability has been achieved, with mean NRMS values of less than 5% for data acquired during the same season. This level of repeatability is comparable to data acquired in marine 4D surveys and has resulted in the detection of the small 4D signal caused by CO2 injection. Frequent monitor surveys, with one full survey acquired every four weeks, shows the CO2 plume growing over time with increasing injection volume. While the observed CO2 plume largely correlates to available engineering data, discrepancies have been identified when compared with the predicted seismic response based on the reservoir simulation model. This indicates that 4D seismic can be used to constrain the reservoir model, yielding a better history match and improved predictions to enable more informed engineering decisions to be made. This is the first successful application of seismic monitoring of a carbonate reservoir in an area renowned for poor seismic data quality. To overcome the challenges, a novel hybrid acquisition system using buried sensors and surface sources was developed. Advances in the seismic processing workflow were also required to bring the 4D noise down to a level that enabled detection of the CO2 injection.

In 2015, Saudi Aramco started a CO2 Water-Alternating-Gas (WAG) EOR pilot project in an onshore carbonate reservoir. To monitor lateral expansion of the CO2 plume, the area was instrumented with a hybrid surface/downhole permanent seismic monitoring system. This system consists of over 1000 buried seismic sensors at a depth of around 70 m, below the the depth of expected weathering layer to mitigate the time-lapse noise. Despite receiver burial, seismic data still suffers from numerous challenges including: significant amounts of high-amplitude coherent noise such as guided waves, mode conversions, and scattered energy; amplitude variations over space and time caused by source and receiver coupling; variability of wavelet shape and arrival times due to seasonal near-surface variations; and low signal-to-noise ratio (SNR). A novel processing workflow was designed for 4D processing of such data. The workflow involves five critical processes. First, the high-amplitude coherent noise is eliminated using FK-based techniques that are 4D compliant to preserve the reservoir changes between repeated seismic surveys. Second, a four-term joint surface-consistent amplitude-scaling algorithm resolves the amplitude variations. The algorithm allows both source and receiver terms to have different scalars for the same positions, but it restricts the other two terms to be position-invariant over different time-lapse surveys, as the window of analysis does not include the reservoir. This is to guarantee that the source and receiver terms are survey-dependent while the other two terms are survey-independent. Thus, the amplitude variability is linked to source and receiver positions over space and time. It also assures that the reservoir changes are not affected by changes in the overburden. Third, wavelet shape variations are addressed using a four-term joint surface-consistent spiking deconvolution algorithm that applies similar principle as the scaling algorithm. Fourth, the small variations in reflection times between different surveys (4D statics) caused by seasonal variations are corrected by a specialized surface-consistent residual statics algorithm using a common pilot derived from the base survey. Fifth, the pre-stack data is supergrouped to enhance the signal-to-noise ratio and repeatability. The processing workflow has been applied to frequent land 3D seismic data acquired over a CO2 WAG EOR pilot project in Saudi Arabia. As a result, we obtained very repeatable seismic images that may successfully detect small CO2-related changes in a stiff carbonate reservoir.