The application of the Carbon Dioxide Enhanced Oil Recovery (CO 2-EOR) to an offshore oilfield in Vietnam has been investigated through an international joint study between Japan and Vietnam since 2007. In order to reduce and mitigate uncertainties and risks for future field scale application, a CO 2-EOR pilot test was conducted in the Rang Dong field, offshore Vietnam in 2011. The pilot test was conducted as a single-well "Huff-n-Puff" operation which was designed to minimize the test duration, required CO 2 volume to observe the meaningful CO 2-EOR effects and to reduce the risk of corrosion to the existing facility. Over 100 tons of CO 2 were successfully injected into the Lower Miocene sandstone reservoir and the well was flowed back after two days of soaking time. Various parameters were monitored including production rate, bottom-hole pressure and production fluid properties. To investigate the CO 2-EOR effects and for the purpose of monitoring the reservoir fluid saturation change, cased hole pulsed neutron saturation logging was carried out during every stage of the test. These logging operations and their timing were carefully designed to avoid any possibility of fluid contamination and to minimize operation time. Through the analysis of the acquired logs, vertical contrast of preferential CO 2 injection, zonal oil saturation change, oil saturation change with time within single run, etc., were clearly identified. These observations played important roles in evaluating the CO 2-EOR effect in detail by comparing them with the reservoir simulation prediction. In this paper the detailed design for the reservoir fluid saturation monitoring, observed results and three-phase fluid saturation changes wtih time are discussed.

The deformation style of the Torlesse Terrane along the southern Kaikoura coast, South Island, New Zealand, records shallow level deformation processes within an accretionary prism during the Early Cretaceous. The beds exhibit complicated structural features resulting from multistage deformations in a lithological unit, that were intimately related with the dewatering and lithification of terrigenous sediments. The earliest phase of deformation throughout the transect studied was the development of pinch-and-swell structures and boudinage fabrics due to layer-parallel extension while the beds were poorly consolidated. This was followed locally by mesoscopic tight to close recumbent folding. The beds are cut locally by two phases of mudstone intrusions. The earlier phase was initiated by 'in situ' fluidization of mudstone layers, whereas the later phase represented intrusion of siliceous claystone probably derived from an overpressured decollement. Minor faults at high-angles to bedding by layer-normal shortening then disrupted the beds throughout the transect. The deformation was followed by formation of meso-and macroscopic scale open to gentle folds by layer-parallel shortening. Kilometer-scale differential stratal rotations were produced during the final main tectonic phase that occurred in association with postaccretion Neogene regional disturbance.

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Describing cuttings is routine work for wellsite geologists on a drill rig. The time-consuming nature of this analysis and the lack of consistency of the results between different interpreters are the two major concerns for this task. Wellsite geologists spend approximately 70% of their time on cuttings descriptions. In addition, 2 to 3 wellsite geologists are generally assigned to a drilling campaign, and they are replaced at the end of a shift. ML/AI techniques have the potential to solve these issues because of their advantages in prediction speed, objectivity, and consistency. The authors’ aim is to automate the task of cuttings descriptions with ML/AI techniques. We are targeting four lithologies, namely sandstone, mudstone, volcanic (volcanic rocks), and carbonate (carbonate rocks). The cuttings were collected from six wells in the Browse Basin (Australia). Of these four lithologies, a total of 1978 cuttings images were taken under a stereomicroscope. We chose a semantic segmentation technique using Convolutional Neural Network (CNN) algorithms to perform the image classification task. The images were labelled using the open-source annotation software. This annotated data were used for the network training. The labelled images were split into training, validation, and test sets. The accuracy of the trained model was evaluated using the intersection-over-union metric (IOU). The mean IOU of the final model on the validation dataset was 82.3%. Prediction results on the cuttings that are represented by single lithologies are qualitatively very accurate. On the other hand, the prediction for the non-typical lithology (e.g., siltstone, dark-colored volcanic rocks, mixed lithology samples) has room for improvement. The fragments with similar textures (e.g., dark colored volcanic and dark mudstone) are complex for the CNN to identify. The final goal of our project is not only the lithology identification but also the quantitative estimation of lithology abundances in the cuttings. Additional model improvements, such as hyperparameter optimization and significantly more training data, are required to accomplish this task successfully. The trained model for cuttings description has the potential to realize quantitative and high-speed cuttings description. Well-trained AI/ML models have the potential to assist well site geologists by automating the cuttings description process simplifying, speeding up and improving the consistency on the rig floor.