石油資源開発株式会社

Abstract A conventional geological carbon dioxide (CO2) sequestration has risks of leakage and insufficient utilization of storage capacity. These undesirable phenomena are attributed to the separation of CO2 due to gravitational effects. Carbonated water injection is one approach to solve the technical challenges, however it has limited CO2 content per unit volume. Ultrafine bubble (UFB) technology has the potential to increase the CO2 content in the carbonated water, i.e. the water becomes supersaturated with CO2 by dispersing CO2-UFBs. The UFBs less than 1 μm in diameter have a small buoyancy and maintain the stability of them for a long duration. Because CO2-supersaturated water has the potential to suppress the reduction in sweep efficiency caused by the difference in viscosity with in-situ fluid, the risk of insufficient pore space utilization can be minimized. The knowledge of UFB under high pressure has rarely been reported and is crucial for designing CO2-UFB injection systems. Experiments on CO2-UFB under high pressure were carried out in this study. Five additives, i.e. trisiloxane, calcium carbonate particle, oleic acid, ethanol and saponin, were tested to make the UFB and the CO2 content more stable. A Simulation study was also conducted to examine effective injection strategies. As a result, the following conclusions were achieved: (1) 30-40 vol.% of CO2 can be dispersed in water as UFBs and maintained for approximately 120 minutes by using trisiloxane. However, the UFBs at high temperatures become unstable immediately. (2) The other additives, except for calcium carbonate particles, also have an effect on increasing the CO2 content in the water. The maximum increase ratio of the CO2 content in the solubility is 10%. (3) Numerical flow simulations have suggested that the CO2-UFB injection carries minimal risk of CO2-leakage to the surface and can improve sweep efficiency.

This study applies the Unified Ensemble Modeling (UEM) approach to integrate static and dynamic variables into a single workflow, improving models history-match quality, reducing uncertainty, and ensuring geologically plausible models for better reservoir management and decision-making. Traditional history-matching often fails to balance geological realism with production data, leading to biased, unreliable models. This study applies a probabilistic Unified Ensemble Modeling (UEM) workflow to a shallow volcanic oil reservoir in Japan, generating 70 geologically consistent realizations that incorporate static and dynamic uncertainties. The ensemble was refined using Ensemble Smoother with Multiple Data Assimilation (ES-MDA) across three iterations (201 runs), updating permeability, saturation functions, fault transmissibility, and relative permeability to match production data. The approach enhances uncertainty quantification and improves reservoir performance forecasting, offering a more robust foundation for development planning. The proposed study demonstrates the effectiveness of a Unified Ensemble Modeling (UEM) workflow in addressing limitations of traditional history-matching methods. The approach delivered improved geological consistency by incorporating an extension of pluri-gaussian facies modeling, allowing permeability and porosity updates while preserving correlation within static well log data. Probabilistic uncertainty was significantly reduced by iteratively updating 14 static and 54 dynamic parameters, minimizing mismatch between prior and history-matched realizations without overfitting. The original high water cut model predictions were resolved through refinement of relative permeability and saturation functions, aligning forecasts with observed production trends. Fault transmissibility multipliers were dynamically calibrated, enhancing inter-well connectivity and reducing mismatch in reservoir performance. The process achieved history-match convergence within three ES-MDA iterations (201 simulation runs), outperforming traditional workflows in both accuracy and efficiency. The UEM approach supports continuous model refinement and reservoir characterization learnings with new data, enabling faster, more confident decisions. Unlike manual, deterministic workflows, UEM delivers a structured, automated framework that maintains geologic realism while capturing dynamic behaviour. It improves forecast reliability, support operators in optimizing well placement, and reduces risk in complex reservoirs. This study demonstrates how ensemble-based modeling provides a structured, repeatable framework for reducing uncertainty, improving forecast and mitigating risks in complex reservoirs, making it a valuable tool for field development planning. This paper demonstrates that the Unified Ensemble Modeling (UEM) methodology can be effectively applied in vary diverse geological context, integrating geological realism with dynamic calibration. Through iterative updates of 68 parameters across 70 realizations using ES-MDA, the study showcases improved history match quality, reduced uncertainty, and enhanced forecast reliability. It offers a scalable, repeatable approach that advances digital reservoir management—providing actionable insights and filling a critical gap in current industry practices.

We have developed a portable active seismic source (PASS) to monitor CO2 storage reservoirs at a depth of approximately 1 km. Despite its small size, stacking the signals generated by the PASS improves the signal-to-noise ratio of the seismometer data far from the source. The smaller size and lower cost of the PASS enables its permanent deployment in many locations to continuously monitor CO2 storage reservoirs. To achieve continuous monitoring, distributed acoustic sensing (DAS) is also a vital technology. Based on DAS, we can continuously record the signal from the PASS in an extensive area, including within boreholes and offshore fields. Here we report application of the PASS for the borehole DAS system. We confirmed the PASS signal propagation to a depth of similar to 1 km when we used a PASS with 630N at 50 Hz close to the wellhead and recorded the signal by the borehole fiber optic cable. The ability of the system to propagate the PASS signal to a depth of similar to 1 km enables continuous monitoring of most CO2 storage reservoirs with high temporal resolution. Furthermore, deploying multiple PASS systems could improve the spatial resolution of monitoring results. (c) 2023 Society of Chemical Industry and John Wiley & Sons, Ltd.

Abstract Early detection of stuck pipe events is pivotal due to their role as major causes of non-productive time. Although researchers have adopted data-driven approaches, such as machine learning, challenging issues persist, such as limited datasets, limited stuck pipe events, and various causes of stuck pipe. This study proposes a novel machine learning approach that incorporates physical knowledge, explaining universal facts, to overcome the aforementioned challenges. This study creates a prediction model that focuses on the hook load to predict occurrences of stuck pipes. To create the model, we adopted a novel idea that incorporates physical knowledge such as torque and drag models. Because the measured hook load contains uncertainties and is affected by operating conditions, the hook load is expressed using physical knowledge with unknown parameters, and the parameters are determined using data science technologies, including machine learning, based on adjacent historical hook load data. The prediction model is created using only the data in the normal conditions, and the model can express the hook load in normal condition. When the measured hook load exceeds that predicted via the model, it can be assumed that a larger a friction (drag) force is exerted, resulting in a high stuck risk. The deviation, calculated from the predicted and measured hook loads, is acquired, and finally, if the deviation exceeds the normal range determined through data science techniques, including machine learning, the risk of blockage is outputted. This study initially introduces the stuck pipe predictions using both supervised and unsupervised machine learning approaches. Subsequently, we present machine learning integrating physical knowledge and demonstrate early stuck pipe detection using field data containing stuck pipe events. This novel machine learning approach incorporating physical insights may contribute to significant reduction of nonproductive time in drilling operations, potentially preventing well abandonment.

Portable Active Seismic Source (PASS) vibrating system designed to continuously generate weak but precise and rapid vibrations for long periods. The PASS has the capability to produce chirp signals that cover a broad spectrum of frequencies, allowing for customization according to the imaging or monitoring objective. The weak vibrations can be stacked to enhance the signal-to-noise ratio. The two axes design magnifies the vertical motion, limits the horizontal motion (shear waves), and generates high energy compared to the single-axis PASS. The source was designed minimally, reducing the cost of operation to image and monitor geological formations with high spatial resolution. We tasted the source in serval environmental conditions. In this article, we tested PASS using distributed acoustic sensing (DAS) in a borehole for 300 meters in a vertical well, 900 channels of DAS on the surface, and two dense profiles of the FDU 428XL system. Utilizing the two-axes design for PASS reduces the operation time (sweeps) required to achieve clear signal propagation compared to other approaches. The PASS-DAS system, equipped with its dense array of DAS receivers, exhibits the potential for cost-effective high-resolution monitoring applications. The field experiments illustrated that the PASS could be used to image the subsurface efficiently and affordably in challenging environments with restricted accessibility, such as mountains and extraterrestrial regions. Most important, it can effectively monitor geothermal aquifers and Carbon Capture and Storage (CCS) projects for long periods.