Rajesh R. Nair

Bio: Rajesh R. Nair is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Continental margin & Heavy mineral. The author has an hindex of 9, co-authored 47 publications receiving 328 citations. Previous affiliations of Rajesh R. Nair include GlobalFoundries & Indian Institute of Technology Kharagpur.

Laboratory measurements of ultrasonic wave velocities of rock samples and their relation to log data: A case study from Mumbai offshore

Abstract: The characterization of the reservoir rock's geomechanical properties is critical to address wellbore instabilities and subsidence-related issues. To address these issues, lab-derived dynamic and static elastic properties are essential to match the in-situ rock properties. In this study, as part of a new integrated workflow P-wave and S-wave velocities are congregated using ultrasonic transducers for the core plugs, which constitutes mainly carbonates, shales, and both. Mineral composition, shale anisotropy, seismic velocities, and cross plots are studied to understand shear wave splitting. During this study, as a part of 1D mechanical Earth models, rock elastic properties are calculated for 60 wells using petrophysical logs (gamma, density, acoustic and caliper). Also, triaxial loading tests are conducted on 14 specimens collected from the same wells, static Poisson's ratio and static Young's modulus are computed from the stress-strain curves. The major differences are observed between static and dynamic elastic properties calculated from well logs and lab tests. Cohesion and friction angle for rock samples are estimated from the triaxial tests under different confining pressures. The objective of this study is to compare the elastic properties derived from the ultrasonic method with well logs and fill the gaps in the 1D geomechanical model. The combined analysis of elastic properties from different methods provides exciting insights on wellbore stability in anisotropic rock.

Devices with localized strain and stress tuning

Abstract: A device, such as a MEMS device, with stress tuning to achieve a desired stack stress across the wafer. The stress tuning includes trimming a stress compensation layer over a target layer having different stresses in different target layer regions. The trimming may include ion beam trimming to produce a stress compensation layer having different thicknesses over the different target layer regions to balance the stress of the target layer to a desired stress. The desired stress may result in almost zero residual stress to produce an almost flat MEMS device.

Uplift of an Underground Tank in Northern Malabar Region, India

Abstract: An underground reinforced concrete tank was constructed for a project in the southwest region of India. The tank was 90 m x 35 m in plan and 7.3 m deep resting on partly filled-up and partly native soil. During the peak monsoon, a sudden uplifting of the base slab by about 300 mm and subsequent failure of the foundation raft and a partition wall was observed. Laboratory testing was executed and hydrogeological survey was carried out using ground penetrating radar, seismic refraction and infiltrometer testing, and an analytical study was carried out to identify the root cause of the tank uplifting. Based on this study, it was observed that the uplifting and structural failure was essentially due to the peculiar land terrain and soil properties and the development of excess hydraulic head below the bottom of the tank. After considering different options, the rectification measures were carried out by provision of dewatering wells along the tank periphery to release the excess hydrostatic pressure and stabilize the foundation raft. The structural repair of the top of the foundation raft and partition wall was carried out to strengthen the reinforced concrete members. The rectification measures worked well to increase the structural stability of the tank and to prevent build-up of excess hydrostatic pressure preventing uplift and subsequent damage in the future.

Unified fracking device for enhanced recovery from conventional reservoirs, hydrates and shales

Abstract: A system (100) for generating shock waves in a well bore (102) includes a fracking gun (110) and a coupler (112) for detachably coupling the fracking gun (110) to a wire line (106) from an external unit (104). The coupler (112) isolates the wire line (106) from the fracking gun (110). The fracking gun (110) includes a cartridge (200), a gas filled cylinder (202) having an internal pressure maintained below the pressure present inside the well bore (102). Further, the fracking gun (110) includes explosive pods provided with explosive charges to rupture the high stress concentrated regions present on the surface of the cartridge (200) and the gas filled cylinder (202) to generate shock waves. The generated shock waves are followed by negative blast waves that results in elongation of existing perforations, creation of primary and secondary fractures, and also opening of clogged pores.

Digital isochrons of the world's ocean floor

Abstract: We have created a digital age grid of the ocean floor with a grid node interval of 6 arc min using a self-consistent set of global isochrons and associated plate reconstruction poles. The age at each grid node was determined by linear interpolation between adjacent isochrons in the direction of spreading. Ages for ocean floor between the oldest identified magnetic anomalies and continental crust were interpolated by estimating the ages of passive continental margin segments from geological data and published plate models. We have constructed an age grid with error estimates for each grid cell as a function of (1) the error of ocean floor ages identified from magnetic anomalies along ship tracks and the age of the corresponding grid cells in our age grid, (2) the distance of a given grid cell to the nearest magnetic anomaly identification, and (3) the gradient of the age grid: i.e., larger errors are associated with high age gradients at fracture zones or other age discontinuities. Future applications of this digital grid include studies of the thermal and elastic structure of the lithosphere, the heat loss of the Earth, ridge-push forces through time, asymmetry of spreading, and providing constraints for seismic tomography and mantle convection models.

Ministry of Statistics and Programme Implementation

Abstract: Meeting: International Seminar on Information and Communication Technology Statistics, Seoul, Republic of Korea, 19-21 July 2010

Machine learning for data-driven discovery in solid Earth geoscience

Abstract: BACKGROUND The solid Earth, oceans, and atmosphere together form a complex interacting geosystem. Processes relevant to understanding Earth’s geosystem behavior range in spatial scale from the atomic to the planetary, and in temporal scale from milliseconds to billions of years. Physical, chemical, and biological processes interact and have substantial influence on this complex geosystem, and humans interact with it in ways that are increasingly consequential to the future of both the natural world and civilization as the finiteness of Earth becomes increasingly apparent and limits on available energy, mineral resources, and fresh water increasingly affect the human condition. Earth is subject to a variety of geohazards that are poorly understood, yet increasingly impactful as our exposure grows through increasing urbanization, particularly in hazard-prone areas. We have a fundamental need to develop the best possible predictive understanding of how the geosystem works, and that understanding must be informed by both the present and the deep past. This understanding will come through the analysis of increasingly large geo-datasets and from computationally intensive simulations, often connected through inverse problems. Geoscientists are faced with the challenge of extracting as much useful information as possible and gaining new insights from these data, simulations, and the interplay between the two. Techniques from the rapidly evolving field of machine learning (ML) will play a key role in this effort. ADVANCES The confluence of ultrafast computers with large memory, rapid progress in ML algorithms, and the ready availability of large datasets place geoscience at the threshold of dramatic progress. We anticipate that this progress will come from the application of ML across three categories of research effort: (i) automation to perform a complex prediction task that cannot easily be described by a set of explicit commands; (ii) modeling and inverse problems to create a representation that approximates numerical simulations or captures relationships; and (iii) discovery to reveal new and often unanticipated patterns, structures, or relationships. Examples of automation include geologic mapping using remote-sensing data, characterizing the topology of fracture systems to model subsurface transport, and classifying volcanic ash particles to infer eruptive mechanism. Examples of modeling include approximating the viscoelastic response for complex rheology, determining wave speed models directly from tomographic data, and classifying diverse seismic events. Examples of discovery include predicting laboratory slip events using observations of acoustic emissions, detecting weak earthquake signals using similarity search, and determining the connectivity of subsurface reservoirs using groundwater tracer observations. OUTLOOK The use of ML in solid Earth geosciences is growing rapidly, but is still in its early stages and making uneven progress. Much remains to be done with existing datasets from long-standing data sources, which in many cases are largely unexplored. Newer, unconventional data sources such as light detection and ranging (LiDAR), fiber-optic sensing, and crowd-sourced measurements may demand new approaches through both the volume and the character of information that they present. Practical steps could accelerate and broaden the use of ML in the geosciences. Wider adoption of open-science principles such as open source code, open data, and open access will better position the solid Earth community to take advantage of rapid developments in ML and artificial intelligence. Benchmark datasets and challenge problems have played an important role in driving progress in artificial intelligence research by enabling rigorous performance comparison and could play a similar role in the geosciences. Testing on high-quality datasets produces better models, and benchmark datasets make these data widely available to the research community. They also help recruit expertise from allied disciplines. Close collaboration between geoscientists and ML researchers will aid in making quick progress in ML geoscience applications. Extracting maximum value from geoscientific data will require new approaches for combining data-driven methods, physical modeling, and algorithms capable of learning with limited, weak, or biased labels. Funding opportunities that target the intersection of these disciplines, as well as a greater component of data science and ML education in the geosciences, could help bring this effort to fruition. The list of author affiliations is available in the full article online.

The breakup of East Gondwana: Assimilating constraints from Cretaceous ocean basins around India into a best‐fit tectonic model

Abstract: Published models for the Cretaceous sea!oor-spreading history of East Gondwana result in unlikely tectonic scenarios for at least one of the plate boundaries involved and/or violate particular constraints from at least one of the associated ocean basins. We link East Gondwana spreading corridors by integrating magnetic and gravity anomaly data from the Enderby Basin off East Antarctica within a regional plate kinematic framework to identify a conjugate series of east-west-trending magnetic anomalies, M4 to M0 (~126.7–120.4 Ma). The mid-ocean ridge that separated Greater India from Australia-Antarctica propagated from north to south, starting at ~136Ma northwest of Australia, and reached the southern tip of India at ~126 Ma. Sea!oor spreading in the Enderby Basin was abandoned at ~115 Ma, when a ridge jump transferred the Elan Bank and South Kerguelen Plateau to the Antarctic plate. Our revised plate kinematic model helps resolve the problem of successive two-way strike-slip motion between Madagascar and India seen in many previously published reconstructions and also suggests that sea!oor spreading between them progressed from south to north from 94 to 84 Ma. This timing is essential for tectonic !ow lines to match the curved fracture zones of the Wharton and Enderby basins, as Greater India gradually began to unzip from Madagascar from ~100 Ma. In our model, the 85-East Ridge and Kerguelen Fracture Zone formed as conjugate !anks of a "leaky" transform fault following the ~100Ma spreading reorganization. Our model also identi"es the Afanasy Nikitin Seamounts as products of the Conrad Rise hotspot.