Conventional studies of petroleum basins associate oil generation with the gradual burial of organic-rich sediments. These classical models rely on the interplay between pressure, temperature, and the time required for organic matter transformation to oil and gas. These processes usually occur over geological timescales, but may be accelerated by rapid reactions when carbon-rich sediments are exposed to migrating magmatic fluids. The spectacular Lusi eruption (north-east Java, Indonesia) is the surface expression of the present-day deep interaction between volcanic and sedimentary domains. Here we report the ongoing generation of large amounts of hydrocarbons induced by a recent magmatic intrusion from the neighbouring Arjuno-Welirang volcanic complex. We have investigated a unique suite of oil and clast samples, and developed a detailed conceptual model for the complex hydrocarbon migration history in this part of the basin by integrating multidisciplinary techniques. Our results show that palynology, organic petrology, and chlorite microthermometry are the most sensitive geothermometers for basins affected by recent magmatic activity. These findings further our understanding of the driving mechanisms fueling the world’s largest active mud eruption and provide a unique dataset to investigate modern hydrocarbon generation processes.

The Tertiary back‐arc sedimentary basin in East Java (Indonesia) hosts a large variety of piercement structures and hydrocarbon fields. Some of the latter (Wunut, Tanggulangin, Carat, Watudakon) are located a few kilometers away from the Arjuno‐Welirang volcanic complex and neighboring Lusi, the largest active sediment‐hosted hydrothermal system on Earth. In order to investigate interactions between volcanic and sedimentary settings, we performed gas sampling on these four shallow (200‐ to 1,000‐m depth) petroleum fields. The fields around Lusi are dominated by thermogenic gas that was altered during biodegradation processes. The helium isotope ratios (3He/4He) are as high as 6.7 RA, which is remarkably similar to those measured at the fumaroles of the adjacent volcanic complex (R = 7.3 RA) and at the Lusi site (up to 6.5 RA). This highlights the pervasive outgassing of mantle‐derived fluids in the sedimentary basin. Despite these two systems sharing the same mantle‐derived helium source, their hydrocarbons have two different genetic histories: Lusi hydrocarbon gas has been more recently generated and is less molecularly and isotopically fractionated, while the gas trapped in the reservoirs is older and more altered. Unlike Lusi, the hydrocarbon fields contain small amounts of CO2 resulting from biodegradation processes. The Watukosek fault system, originating from the Arjuno‐Welirang volcanic complex and extending toward the northeast of Java, intersects Lusi and the hydrocarbon fields. This network of faults controls the migration of mantle‐derived fluids within the sedimentary basin, feeding the focused venting at the Lusi site and promoting the slower and pervasive migration in the reservoirs.

Drilling horizontal wells in complex formations is always a challenging task. The development of deep and ultra-deep azimuthal resistivity tools has largely improved the accuracy of the wellbore placement in the target zone. The enhanced imaging provided by the stochastic inversion of the azimuthal resistivity data can be applied for mapping both the internal reservoir structure and fluid contacts in the field. Major oil and gas service companies provide the operator with azimuthal resistivity tools and develop their own algorithms for resistivity data processing. Commonly services companies process azimuthal resistivity data internally. We have developed a vendor-independent stochastic inversion method that is applicable to almost any deep-azimuthal resistivity tools. This module allows operators to carry out real-time geosteering, as well as pre-job and post-job data analyses independently from the service company. This paper demonstrates the examples of the azimuthal resistivity data interpretation using synthetic data and actual data from the well offshore Norway. Calculated inversion models, based on actual data, allowed mapping of the oil-water contact and discontinuities in the reservoir that take place at the resistivity contrast boundaries. The application of this technology can increase the percentage of the horizontal well in the pay zone while letting the operator cut drilling costs through optimizing bottom hole assembly and use more advanced interpretation practices.

Logging-while-drilling (LWD) ultra-deep resistivity technology can explore the reservoir on a similar scale to seismic, so interpreted resistivity models can be combined with seismic sections to enable oil field operators to delineate pay zones better, improve reservoir understanding, and eventually achieve higher reservoir contact value by proactive geosteering. Currently, there is no industry-adopted processing software which supports different ultra-deep tools. This paper presents the first vendor-independent, gradient-based stochastic approach for ultra-deep data inversion while drilling. Industry literature review was performed to determine parameters of ultra-deep tools, investigate their responses, and add them to the list of supported devices. Inversion algorithm is based on stochastic Monte Carlo method with reversible jump Markov chains and can be launched automatically without prior assumptions about the reservoir structure. Finally, it provides an ensemble of unbiased 1D formation models explaining the measurements as well as uncertainty estimates of model parameters. Parallel running of several Markov chains on multiple CPUs with both gradient-based sampling and exchanging their states makes the algorithm computationally effective and helps to avoid sticking in local optima. The proposed approach enables gathering of ultra-deep tools from different vendors under a common interface, along with other resistivity tools, joint processing various resistivity data with the same inversion workflow, and representation of inversion deliverables in unified format. Due to the large formation volume being investigated, the ultra-deep readings become complex. To be interpreted, such responses require multi-layer models as well as special multi-parametric inversion software. Working in high-dimensional parameter space, stochastic Monte Carlo inversion algorithms might not be effective due to the limitation of sampling procedure that usually generates new samples through the random perturbation of the few model parameters and does not consider their relations with other model parameters. This may lead to a high rate of proposal rejections and a lot of unnecessary calculations. To overcome this issue and guarantee real-time results, the presented approach employs Metropolis-adjusted Langevin technique which evaluates the gradient of posterior probability density function and generates proposals with a higher posterior probability of being accepted. Additionally, a special fast semi analytical solver is utilized to compute the gradient simultaneously with tool responses, with almost no extra computational costs. Application of the developed software is shown on synthetic scenarios and case studies from Norwegian natural gas and oil fields. The presented approach is identified as the first vendor-independent gradient-based inversion algorithm operating with any measurements of ultra-deep and deep azimuthal resistivity tools available on the market. The algorithm is high-performance and ensures real-time inversion results even in case of multi–layer formation models required to interpret ultra-deep measurements. The software may help oil field operators to resolve reservoir structure at a larger scale and pursue a more informed reservoir development strategy thus making more confident geosteering decisions.

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