Petrophysical core-based zonation of OW oilfield in the Bredasdorp Basin South Africa

This study aims to generate rock units based on core permeability and porosity of OW oilfield in the Bredasdorp Basin offshore South Africa. In this study, we identified and classified lithofacies based on sedimentology reports in conjunction with well logs. Lucia's petrophysical classification method is used to classify rocks into three classes. Results revealed three lithofacies as A (sandstone, coarse to medium-grained), B (fine to medium-grained sandstone), and C (carbonaceous claystone, finely laminated with siltstone). Lithofacies A is the best reservoir quality and corresponds to class 1, while lithofacies B and C correspond to class 2 and 3, which are good and poor reservoir quality rock, respectively. An integrated reservoir zonation for the rocks is based on four different zonation methods (Flow Zone indicator (FZI), Winland r35, Hydraulic conductivity (HC), and Stratigraphy modified Lorenz plot (SMLP)). Four flow zones Reservoir rock types (RRTs) were identified as RRT1, RRT3, RRT4, and RRT5, respectively. The RRT5 is the best reservoir quality composed of a megaporous rock unit, with an average FZI value between 5 and 10 µm, and HC from 40 to 120 mD/v3, ranked as very good. The most prolific flow units (RRT5 and RRT4 zones) form more than 75% of each well's flow capacities are supplied by two flow units (FU1 and FU3). The RRT1 is the most reduced rock quality composed of impervious and nanoporous rock. Quartz is the dominant framework grain, and siderite is the dominant cement that affects flow zones. This study has demonstrated a robust approach to delineate flow units in the OW oilfield. We have developed a useful regional petrophysical reservoir rock flow zonation model for clastic reservoir sediments. This study has produced, for the first time, insights into the petrophysical properties of the OW oilfield from the Bredasdorp Basin South Africa, based on integration of core and mineralogy data. A novel sandstone reservoir zonation classification criteria developed from this study can be applied to other datasets of sandstone reservoirs with confidence.

Uncertainty and Sensitivity Analysis on Clastic Systems: Integrated Workflows for Understanding and Managing the Risk, A Case Study in Tuwayil Formation UAE

This study has been done on Late Cretaceous tight clastic reservoir located south west of Abu Dhabi city with the border with Saudi Arabia. The field was discovered in the 1960s and a few wells were drilled subsequently. The Tuwyail clastic reservoir is characterized as tight with average permeability below 1 mD. The trap is identified as structural trap as north south anticline with gentle dip in both sides. Total of six wells were drilled targeting Tuwyail reservoir which part is of Wasia group. However, assessing potential of this accumulation poses a great challenge not only in terms of understanding of the depositional system which still unknown before but also in terms of quality of the legacy data like well data that impact the modeling studies. The aim of this paper is to provide an insight on integrated workflows for assessing the different uncertainties on clastic systems with limited data, focused on the most important sensitivities parameters impacting the oil in place, like reservoir proportions, free water level [FWL] and lateral distributions of the sedimentary elements within the area of interest which playing a big rules in future developing of the field. Before moving to full field development a full uncertainty and sensitivity analyses were conducted for the Tuwayil reservoir to find the highly uncertain parameters that impacting the future development of the reservoir, in the same time the main challenges is the limited data with low quality as the wells had been drilled in 60s with limited technology at that time and the core data were left in a bad conditions since the filed was left behind.

Indirect Estimation of Clastic Reservoir Rock Grain Size from Wireline Logs Using a Supervised Nearest Neighbor Algorithm: Preliminary Results

Reservoir rock textural properties such as grain size are typically estimated by direct visual observation of the physical texture of core samples. Grain size is one of the important inputs to petrophysical characterization, sedimentological facies classification, identification of depositional environments, and saturation models. A continuous log of grain size distribution over targeted reservoir sections is usually required for these applications. Core descriptions are typically not available over an entire targeted reservoir section. Physical core data may also be damaged during retrieval or due to plugging. Alternative methods proposed in literature are not sustainable due to their limitations in terms of input data requirements and inflexibility to apply them in environments with different geological settings. This paper presents the preliminary results of our investigation of a new methodology based on machine learning technology to complement and enhance the traditional core description and the alternative methods. We developed and optimized supervised machine learning models comprising K-nearest neighbor (KNN), support vector machines (SVM), and decision tree (DT) to indirectly estimate reservoir rock grain size for a new well or targeted reservoir sections from historical wireline logs and archival core descriptions. We used anonymized datasets consisting of nine wells from a clastic reservoir. Seven of the wells were used to train and optimize the models while the remaining two were reserved for validation. The grain size types range from clay to pebbles. The performance of the models confirmed the feasibility of this approach. The KNN, SVM, and DT models demonstrated the capability to estimate the grain size for the test wells by matching actual data with a minimum of 60% and close to 80% accuracy. This is an accomplishment taking into account the uncertainties inherent in the core analysis data. Further analysis of the results showed that the KNN model is the most accurate in performance compared to the other models. For future studies, we will explore more advanced classification algorithms and implement new class labeling strategies to improve the accuracy of this methodology. The attainment of this objective will further help to handle the complexity in the grain size estimation challenge and reduce the current turnaround time for core description.

Redefining Reservoir Architecture of Hydrocarbon-Bearing Ngrayong Formation in Banyu Urip Area:New Insights to Unravel Hidden Potential in East Java Basin

Results of Banyu Urip (BU) carbonate exploration, appraisal and development drillings revealed the existence of hydrocarbon-contained in Serravallian deep-water clastic reservoir on top of the primary BU carbonate reservoir. This clastic reservoir is equivalent to the Ngrayong Formation in East Java Basin which is widely known as a mature exploration target and consists of a wide range of depositional environment from fluvio-deltaic (northern part of the Basin) to basin floor (southern part of the basin) with various reservoir quality. However, after a century of exploration activities in East Java Basin, commercial discoveries in the Ngrayong Formation are still considered insignificant (approximately 330 MMboe) (Mazied et al. 2016). This probably due to complex reservoir architecture posted high uncertainty of its reservoir presence, distribution, and quality as well challenging on their dynamic aspects such as un-known hydrocarbon connectivity, un-even contacts and low-deliverability. This paper will present new insights and the potential of Ngrayong clastic opportunity in BU area based on static and dynamic data including BU wells, newly reprocessed 3D seismic data, conventional core and thin sections, as well as integrated geologic and geophysical analyses. Integration of the available dataset suggest the presence of stacked deep water channels and deep water lobes systems. The distribution of stacked channels and lobes seem to be more predictive and widespread, hence providing a better understanding of its reservoir distribution. Furthermore, well data indicates approximately total of 100m net stacked clastic reservoirs consist of mixed carbonate-clastic materials, and have good reservoir pressure connectivity with the carbonate reservoir underneath. This mixed clastic-carbonate system in Ngrayong Formation is diagenetically-altered, and this diagenesis process plays as an important roles in modifying reservoir quality. Although carbonate cement and diagenetic overprint impose challenging reservoir quality prediction, a dissolution creates better reservoir quality, generates excess permeability and produces high flow reservoir. Detail study of reservoir architecture and diagenesis process are critical to better assess volumetric and development opportunity. These key components will open up new paradigm and essential for successful of Ngrayong Formation exploration in East Java Basin in order to contribute to the country’s energy demand.