Recent maturity in oil and gas exploration has oriented the interests of the industry towards geophysically severer geological targets, including deep and complicated structures or high impedance rocks. In addition, more sophisticated reservoir managements have been required to optimize field developments and suppress operational risks by involving more geophysical measures sensing the subsurface condition. The focuses in technology developments for data processing and analysis therefore include advanced seismic imaging, quantitative interpretation, and reservoir monitoring. Advancements in field recording technologies, in parallel with the efforts for reducing data volume, deliver more "perfect" seismic data with broader frequency and spatial wavenumber, enabling those new processing and analysis technologies.

In recent years, Marine controlled source electromagnetic（CSEM）technology has played an effective role to reduce the exploration risk offshore. Meanwhile, the application of Marine CSEM is not always applicable due to its expensive survey cost and difficulty of quality control for data acquisition and analysis. Therefore, the authors constituted the research consortium and advance the Marine CSEM technological development based on the expertise cultivated at university and research institute. In this paper, we discuss the acquisition, processing, inversion, and evaluation of the data obtained during the demonstration survey.

This paper focuses on a high accuracy permanent reservoir monitoring system that integrates a permanent seismic source named accurately controlled routinely operated signal system（ACROSS）and a fiber optic sensing technology called distributed acoustic sensing（DAS）. To evaluate the effectiveness and benefits of this system, we have conducted a DAS-VSP data acquisition demonstration test at the Aquistore CO₂ storage site in Saskatchewan, Canada. We have acquired four monitoring data sets in this field since 2016 when ACROSS was moved to a location about 750 m away from the observation well. During data acquisition, ACROSS was remotely controlled from Japan to reduce the HSE risk and cost. We constructed an efficient data processing flow including ACROSS signal processing, data matching, VSP data processing and 4D noise suppression. A 4D response evaluation method was established using two different types of repeatability indexes. The data acquisition, processing and evaluation were successful and a high- repeatability seismic section was obtained. In addition, we performed advanced data acquisition using a wireline DAS method and data processing using reverse time migration（RTM）. Lastly, we compared the latest data processing results with 3D seismic monitoring results acquired in the same time and discussed future prospects of reservoir monitoring in a CCUS and EOR field. We think that our monitoring system will be implemented as a useful reservoir monitoring system, so we plan to continue associated research, including the preparation for the new data acquisition in 2022.

In the conventional time-lapse VSP, a sensor array using geophone or hydrophone is permanently or temporarily installed in a well under high temperature and high pressure environment, and the baseline data is recorded over several hours to several days. Sensor arrays become more expensive in proportion to the number of sensors, and only very limited sensors can record without aging in high temperature environments above 150 °C. Therefore, it is possible to observe only in the section within the operating temperature range of the sensor array. Starting from the day when the baseline data was recorded, the data should be recorded again after fixed elapsed time of six months or one year, and then compared with the baseline data in order to observe the movement of fluid in the formation. If the sensor fails, the data for that section will not be available until the sensor is replaced. When using the VSP downhole tool, it can be difficult to place the sensor at exactly the same position as the baseline, and subtle sensor misalignment can affect time-lapse data sometimes. Permanent installation of the sensor is ideal from the perspective of data comparison. In the case of DAS, an optical fiber that acts as sensor is permanently installed in the well. Generally, an optical fiber with temperature rating of 200 °C is passed through a thin stainless steel tube called Control Line and installed on the outside or inside of the casing. Fiber optics are much cheaper than traditional sensor arrays, almost no aging, and can cover almost any section of the well. Taking advantage of these, time-lapse VSP using DAS has begun to be actively used as a practical method in recent years. There are potential issues need to be considered for successful DAS time-lapse VSP to be explained.

In recent years, high-performance computing has played a significant role in the analysis for various kinds of big data. It also has been crucial for the data analysis and processing in upstream oil industry, especially seismic data processing, because huge computational resources are required to apply high-end algorithms, such as full waveform inversion and least-squares reverse time migration, to large-scale 3D seismic data. However, high investment and advanced expertise are indispensable for constructing and maintaining such computational environment. Cloud computing is a highly potential tool to address these challenges and realize seismic processing for large-scale 3D data. In this report, we show the case studies for 3D seismic data processing by using a cloud computing service with a seismic processing software, and then discuss the validity and issue of this approach.

It is challenging to develop thin oil rim reservoirs economically using conventional wells. Horizontal wells are now widely used to overcome the shortcomings of vertical wells. The deciding factor in ensuring successful horizontal wells application is optimum well placement. However, the conventional optimization approach is time and resource-intensive. A data-driven approach was proposed to optimize heel and toe locations by deploying a deep learning model. A synthetic database comprised of nine fundamental parameters that influence recovery mechanisms in thin oil reservoirs was generated to train the model. The accuracy and computation time of a deep-learning model trained on a synthetic database were compared to a novel optimization method that combines a genetic algorithm and a particle swarm optimization（hybrid GA-PSO）algorithm. The deep-learning model predicted optimum well placement（heel and toe points）with an accuracy comparable to the hybrid GA-PSO algorithm. Furthermore, the prediction obtained by the deep-learning model takes significantly less computation time than the hybrid GA-PSO algorithm. The developed optimization method offers a rapid and reliable initial guess of well placement for detailed optimization by simulation. The developed model is universally applicable for various thin oil rim characteristics, especially in the scarcity of data to build a reliable reservoir model.

The volumetric method is the basis of carbon dioxide（CO₂）storage capacity estimation in many organizations implicitly assuming the storage formation is an open system. The compressibility method is another popular method among engineers assuming a closed system. The impacts of boundary conditions on CO₂ injection performance and storage capacity were investigated by literature survey and numerical simulations. The key conclusions of the investigation are summarized as follows: 1）a closed system has a small storage efficiency due to the constraints of pressure build-up: typically in the order of 0.5% or less of effective pore space, 2）a semi-closed system with finite seal permeability can be either effectively a closed system or an open system depending on seal permeability: 10^－5 md or less for a closed system and 10^－2 md or larger for an open system in the assumed case, 3）CO₂ flow from the storage formation to seal formations can be limited due to the difference in capillary pressure between the storage and seal formations even if the seal permeability is high enough to allow sufficient crossflow of formation water for pressure relaxation, 4）permeability cut-off criteria for the compressibility method can be smaller than that for the volumetric method because the rocks with an certain range of permeability do not allow CO₂ inclusion but can contribute pressure relaxation, 5）long-term fall-off data can include useful information to estimate the boundary conditions: e.g. the shut- in pressure recovering to the initial hydrostatic pressure together with a decline in pressure derivative in late time may indicate communication between the storage formation and overlaying and/or underlaying large aquifers.

The study finally highlights the importance of representing lateral and vertical boundary conditions appropriately and recommends modeling the whole reservoir complex consisting of not only the target storage formation, but also overlining/underling seal formations and aquifers for CO₂ storage simulations.

The world has changed its direction widely towards the realization of the CO₂-free society by the Paris Agreement issued in 2015. In Japan, prior to the Paris Agreement, the usage of large amount of hydrogen was proposed by the several sectors through the hydrogen and fuel cell strategy road map issued in June, 2014. Also, the Prime Minister Suga declared the realization of carbon neutral by 2050 at the time of his policy speech in October, 2020 and demonstration of CO₂-free hydrogen supply chain became urgent need. Under those situations, CO₂-free Hydrogen Energy Supply-chain Technology Research Association has been established by the four companies comprises Iwatani Corporation, Kawasaki Heavy Industries Ltd., Shell Japan Limited and Electric Power Development Co., Ltd. in 2016 towards the realization of commercial hydrogen supply chain in 2030. At the moment, research and development（R&D） has been carried out for the demonstration in 2021 through the adoption of Demonstration Project for Establishment of Mass Hydrogen Marine Transportation Supply Chain Derived from unused Brown Coal funded by the subject setting type industrial technical development subsidy of NEDO.

In this demonstration project, the following R&D has been carried out which is "brown coal gasification technology",

"technology of loading distance transportation of mass liquefied hydrogen" and "liquefied hydrogen loading and unloading technologies".

The progress status of R&D is reported below.