Distributed Acoustic Sensing (DAS) has emerged as a groundbreaking technology in seismology, transforming fiber-optic cables into dense, cost-effective seismic monitoring arrays. DAS makes use of Rayleigh backscattering to detect and measure dynamic strain and vibrations over extended distances. It can operate using both pre-existing telecommunication networks and specially designed fibers. This review explores the principles of DAS, including Coherent Optical Time Domain Reflectometry (COTDR) and Phase-Sensitive OTDR (ϕ-OTDR), and discusses the role of optoelectronic interrogators in data acquisition. It examines recent advancements in fiber design, such as helically wound and engineered fibers, which improve DAS sensitivity, spatial resolution, and the signal-to-noise ratio (SNR). Additionally, innovations in deployment techniques include cemented borehole cables, flexible liners, and weighted surface coupling to further enhance mechanical coupling and data accuracy. This review also demonstrated the applications of DAS across earthquake detection, microseismic monitoring, reservoir characterization and monitoring, carbon storage sites, geothermal reservoirs, marine environments, and urban infrastructure surveillance. The study highlighted several challenges of DAS, including directional sensitivity limitations, vast data volumes, and calibration inconsistencies. It also addressed solutions to these problems, such as advances in signal processing, noise suppression techniques, and machine learning integration, which have improved real-time analysis and data interpretability, enabling DAS to compete with traditional seismic networks. Additionally, modeling approaches such as full waveform inversion and forward simulations provide valuable insights into subsurface dynamics and fracture monitoring. This review highlights DAS’s potential to revolutionize seismic monitoring through its scalability, cost-efficiency, and adaptability to diverse applications while identifying future research directions to address its limitations and expand its capabilities.

Sand control is an ongoing challenge in numerous hydrocarbon-producing wells in sand-rich reservoirs. Sand production in these wells can cause damage to equipment, reduce production rates, and lead to erosion that can damage subsea equipment, production equipment, well completions, and surface facilities. This problem can compromise the mechanical integrity of the well, resulting in reduced hydrocarbon production and increased operating expenses. This review evaluates various sand production mechanisms, including geological and mechanical production methodologies, and fluid-related aspects, which are thoroughly investigated to offer a thorough understanding of the complexity of the issue and the state of sand prediction approaches. Empirical correlations, numerical simulations, and analytical models are among the sand production prediction techniques critically assessed in this study. The benefits, drawbacks, and suitability of these techniques for various reservoir environments are discussed. Furthermore, the potential benefits of combining Fiber optic (FO) technologies and machine learning (ML) for real-time monitoring and mitigation are highlighted. This integrated strategy has the potential to transform sand control practices of the industry, as demonstrated by case studies and new research that highlights its effectiveness. The future vision outlined in this review includes developments in automation, data processing methods, and sensor technologies, which should improve the precision and dependability of sand production predictions and mitigation. In conclusion, this review paper provides an extensive analysis of the current level of prediction techniques, as well as the mechanisms behind sand production in oil and gas wells. This highlights how real-time, data-driven solutions for monitoring and addressing sand production problems may be provided by FO and ML, which can ultimately lead to safer and more effective hydrocarbon recovery operations.

The advent of fiber optic technology in geophysics exploration has grown in its use in the exploration, production, and monitoring of subsurface environments, revolutionizing the way data are gathered and interpreted critically to speed up decision-making and reduce expense and time. Distributed Acoustic Sensing (DAS) has been increasingly utilized to build relationships in complex geophysics environments by utilizing continuous measurement along fiber optic cables with high spatial resolution and a frequency response of up to 10 KHz. DAS, as fiber optic technology examining backscattered light from a laser emitted inside the fiber and measuring strain changes, enables the performance of subsurface imaging in terms of real-time monitoring for Vertical Seismic Profiling (VSP), reservoir monitoring, and microseismic event detection. This review examines the most widely used fiber optic cables employed for DAS acquisition, namely Single-Mode Fiber (SMF) and Multi-Mode Fiber (MMF), with the different deployments and scopes of data used in geophysics exploration. Over the years, SMF has emerged as a preferred type of fiber optic cable utilized for DAS acquisition and, in most applications examined in this review, outperformed MMF. On the other side, MMF has proven to be preferable when used to measure distributed temperature. Finally, the fiber optic cable deployment technique and acquisition parameters constitute a pivotal preliminary step in DAS data preprocessing, offering a pathway to improve imaging resolution based on DAS measurement as a future scope of work.

Accurate prediction of pore pressure (PP) is crucial for optimizing drilling and reservoir management operations. This study investigates PP prediction in the West Baram Delta, Offshore Sarawak, a mature oil and gas field. The field exhibits varying depths of top overpressure zones, emphasizing the need for reliable PP predictions before the commencement of drilling operations. Sonic transit time, gamma-ray, and density logs are used to establish shale compaction trends and predict PP using Eaton’s and Miller’s methods. The predicted PP at the wellbore location was validated using a repeat formation tester data from two wells. Results showed that the Eaton DT method underestimates PP while the Miller method provides more accurate predictions at deeper overpressure zones. In shallow overpressure zones, where disequilibrium compaction is the dominant mechanism, the Eaton DT and Miller methods yield acceptable PP predictions. A 3D PP model demonstrated a better agreement with Eaton DT predictions at wellbore locations. The onset of overpressure was identified at approximately 2150 m depth. This study's out come provides valuable insights for PP evaluation in the West Baram Delta and in other similar geological settings prior to the drilling operation.

Fibre optic distributed acoustic sensing (FO-DAS) technology is continually gaining attention in the seismic industry due to its cost-effectiveness, longer aperture sensing, minimal maintenance, and its capability to deliver seismic data with high spatial resolution for imaging subsurface stratigraphy and structures. This technology, which consists of an optoelectronic interrogator unit and fibre optic cables as sensors, has been deployed in various geophysical borehole investigations. However, its surface-deployment for subsurface imaging has been limited due to the sensor’s broadside insensitivity. This review significantly advances the understanding of FO-DAS technology, with a particular focus on surface-deployed fiber-optic cables for subsurface imaging. We provide an extensive review of innovative cable configurations, such as inertial member cables, sinusoidal and helical cables, which have been designed and deployed to overcome the broadside sensitivity limitations of straight surface-deployed cables. The potential of surface-deployed fibre optic technology for multicomponent sensing is also explored in detail. In addition, we propose improvements in cable design, cable materials and fibreglass refractive properties for enhanced sensitivity. These insights provide practical ideas for improving fibre-optic DAS systems, pushing the boundaries of surface seismic data acquisition and subsurface imaging.

Sand production in an offshore hydrocarbon wells poses significant operational and integrity challenges, particularly in deviated wells, where complex flow geometries intensify particle transport and erosion risks. The traditional sand-monitoring method utilizes stationary acoustic sensors attached to the production flowline at the surface. However, these sensors provide limited spatial coverage and intermittent measurements, restricting their ability to detect early sanding onset or precisely localize sanding intervals. By combining with vertical seismic profiling (VSP), Distributed Acoustic Sensing (DAS) delivers continuous, high-density data along the entire length of the wellbore and is increasingly recognized as a powerful diagnostic tool for real-time downhole monitoring. This study presents a field application of DAS-VSP for detecting and characterizing sand transport in a deviated offshore production well equipped with 350 distributed fiber-optic channels spanning 0–1983 m true vertical depth (TVD) at 8 m spacing. A multistage workflow was developed, including SEGY ingestion and shot merging, channel and time window selection, trace normalization, and low-pass filtering below 20 Hz. Multi-domain signal analysis, such as RMS energy, spike-based time-domain attributes, FFT, PSD spectral characterization, and time–frequency decomposition, were used to isolate the characteristic im-pulsive low-frequency (<20 Hz) signatures associated with sand impact. An adaptive thresholding and event-clustering scheme was then applied to discriminate sanding bursts from background noise and integrate their acoustic energy over depth. The processed DAS section revealed distinct, depth-localized sand ingress zones within the production interval (1136–1909 m TVD). The derived sand log provided a quantitative measure of sand intensity variations along the deviated wellbore, with normalized RMS amplitudes ranging from 0.039 to 1.000 a.u., a mean value of 0.235 a.u., and 137 analyzed channels within the production interval. These results indicate that sand production is highly clustered within discrete depth intervals, offering new insights into sand–fluid interactions during steady-state flow. Overall, the findings confirm that DAS-VSP enables continuous real-time monitoring of the sanding behavior with a far greater depth resolution than conventional tools. This approach supports proactive sand management strategies, enhances well-integrity decision-making, and underscores the potential of DAS to evolve into a standard surveillance technology for hydrocarbon production wells.