Capture the variation of acoustic impedance property in the Jaisalmer Formation due to structural deformation based on post-stack seismic inversion study: a case study from Jaisalmer sub-basin, India

The Rajasthan basin situates in the western part of India. The basin architecture comprises three significant sub-basins such as Barmer-Sanchor, Bikaner-Nagaur and Jaisalmer. Barmer-Sanchor and Bikaner-Nagaur sub-basins are intracratonic categories, whereas the Jaisalmer sub-basin comes under intracratonic nature. The current study was conducted in the Jaisalmer sub-basin. The study was conducted in two regions in the Jaisalmer sub-basin through a comparative quantitative interpretation study with the help of two vintages seismic surveys. Ghotaru and Bandha are two adjacent areas in the Jaisalmer sub-basin where Ghotaru has seen few hydrocarbon discoveries; however, no such discoveries are encountered in the Bandha area. The current study was concentrated on the Jaisalmer limestone formation in the Jurassic age. The sub-basin and its related study area have been structurally deformed due to various tectonic activities. Structural deformation was played a crucial role in changing the rock property of limestone facies. A post-stack seismic inversion was carried out to capture the rock property changes in the limestone reservoir based on P-impedance values. Development of high P-impedance was observed in the Ghotaru region compared to the Bandha region from this study. A frequency changes of the limestone lithofacies with structural components was also captured in this study. The high impedance limestone lithofacies is a probable hydrocarbon-bearing reservoir unit in the Jaisalmer Formation of the Ghotaru region.

Understanding of Vertical and Horizontal Pressure Barriers in the Naturally Fractured Carbonate Field

The Karachaganak field is a massive reef carbonate structure. The main reservoir is of the late Devonian-Carboniferous age, where sequence stratigraphic cycles of progradation and aggradation defining the growth stages of the carbonate build-up have been revealed. Vertical and horizontal semiconductive barriers was identified in the reservoir during the field development. It was assumed that these barriers are located at the boundaries of the changing depositional cycles, which took place during the reef structure growth. According to the simulation results on a sector model of the reservoir it was determined that the pressure barriers can be developed due to different fracture intensities observed in the reservoir and not because of rock property as such. The reason for the different fracture densities may be associated with compaction during primary diagenesis and may have a sync-depositional nature, which can be seen on carbonate structure outcrops.

Uncertainties in geomechanical models – exhaustive vs. feasible approach

The stress state is a key component for the safety and stability of deep geological repositories for the storage of nuclear waste. For the stability assessment and prediction over the repository lifetime, the stress state is put in relation to the rock strength. This assessment requires knowledge of both the future stress changes and the current in situ stress state. Due to the limited number of in situ stress data records, 3D geomechanical models are used to obtain continuous stress field prediction. However, meaningful interpretation of the stress state model requires quantification of the associated uncertainties that result from the geological, stress and rock-property data. This would require thousands of simulations which in a high-resolution model is called an exhaustive approach. Here we present a feasible approach to reduce computation time significantly. The exhaustive approach quantifies uncertainties that are due to variabilities in stress data records. Therefore, all available data records within a model volume are used individually in separate simulations. Due to the inherent variability in the available data, each simulation represents one of many possible stress states supported by data. A combination of these simulations allows estimation of an individual probability density function for each component of the stress tensor represented by an average value and a standard deviation. If weighting of the data records can be performed, the standard deviation can usually be reduced and the significance of the model result is improved. Alternatively, a range of different stress states supported by the data can be provided with the benefit that no outliers are disregarded, but this comes at the cost of a loss in precision. Both approaches are only feasible since the number of stress data records is limited. However, it is indicated that large uncertainties are also introduced by variabilities in rock properties due to natural intra-lithological lateral variations that are not represented in the geomechanical model or due to measurement errors. Quantification of these uncertainties would result in an exhaustive approach with a high number of simulations, and we use an alternative, feasible approach. We use a generic model to quantify the stress state uncertainties from the model due to rock property variabilities. The main contributor is the Young's module, followed by the density and the Poisson ratio. They affect primarily the σxx and σyy components of the stress tensor, except for the density, which mainly affects the σzz component. Furthermore, a relative influence of the stress magnitudes, the tectonic stress regime and the absolute magnitude of rock properties is observed. We propose to use this information in a post-computation assignment of uncertainties to the individual components of the stress tensor. A range of lookup tables need to be generated that compile information on the effect of different variabilities in the rock properties on the components of the stress tensor in different tectonic settings. This allows feasible quantification of uncertainties in a geomechanical model and increases the significance of the model results significantly.

Using Semi-Supervised Convolutional Neural Networks for Porosity Modeling Over a Fluvio-Deltaic Triassic Gas Field

Robust estimation of rock properties, such as porosity and density, from geophysical data, i.e. seismic and well logs, is essential in the process of subsurface modeling and reservoir engineering workflows. Such properties are accurately measured in a well; however, due to high cost of drilling, such direct measurements are limited in amount and sparse in space within a study area. On the contrary, 3D seismic data illuminates the subsurface of the study area throughoutly by seismic wave propagation; however, the connection between seismic signals and rock properties is implicit and unknown, causing rock property estimation from seismic only to be a challenging task and of high uncertainty. An integration of 3D seismic with sparse wells is expected to eliminate such uncertainty and improve the accuracy of static reservoir property estimation. This paper investigates the application of a semi-supervised learning workflow to estimate porosity from a 3D seismic survey and 36 wells over a fluvio-deltaic Triasic gas field. The workflow is performed in various scenarios, including purely from seismic amplitude, incorporating a rough 6-layer deposition model as a constraint, and training with varying numbers of wells. Good match is observed between the machine prediction and the well logs, which verifies the capability of such semi-supervised learning in providing reliable seismic-well integration and delivering robust porosity modeling. It is concluded that the use of available additional information helps effectively constrain the learning process and thus leads to significantly improved lateral continuity and reduced artifacts in the machine learning prediction. The semi-supervised learning can be readily extended for estimating more properties and allows nearly one- click solution to obtain 3D rock property distribution in a study area where seismic and well data is available.

Efficient Wellbore Optimization Through Rock Property Analysis and Evaluation

There are numerous problems encountered during drilling such as wellbore instability, drilling mud weight estimation, as well as selecting good casing and bit for the drilling operations. It is therefore important to understand and accurately determine the strength of the rock in order to avoid these common drilling problems which are mostly encountered during well operations. It is of paramount importance to determine uniaxial compressive strength (UCS) from core and sonic log data so as to accurately predict rock strength for better well planning. In this work, we were able to obtain a correlation to determine UCS from data obtained from ten (10) wells in different locations in onshore Niger Delta using the regression analysis method. The correlation of UCS versus Poisson's ratio gave R2 value of 90.0%. The R2- value tending towards one (1) indicates that this model can be reliably used to predict ND-UCS and the p<0.05 shows that there is significant relationship between ND-UCS and Poisson's ratio. The model was validated with an entirely different well data and it predicted over 89% rock UCS data when compared to the actual rock UCS data. This study also provides an understanding of the variation in UCS and Poisson's ratio with depth for effective rock property analysis and evaluation. These correlations will help well engineers to make informed decisions on rock strength predictions during well planning and operations as well as manage wellbore stability optimally.

Best Practice of Bit Optimization in a Strong Heterogeneity Conglomeratic Sandstone Reservoir: 8 Years Case History from Juggar Basin, West China

MH oilfield, located in the Junggar Basin, in Xin Jiang Province of northwest China, is the world largest conglomerate reservoir with a fan-delta sedimentary environment. This long-term project can be traced back to 2012, and since then has gone through many technology revolutions and optimizations. At the end of 2017, the drilling performance of one main block inside MH oilfield, M18, was not optimistic when compared with other blocks. The extremely high formation hetergenity of the field made it very challenging to choose the right bit at the right time. This long-term project has brought to light the dedicated, quantifying study of the rock property differences throughout this field and inside each block. To solve this tough bit selection problem, geologic data was interpreted for engineering use. Two lines of data were processed. One was offset analysis based on the current run records to optimize bit designs, and the other was rock property interpretation and simulation to predict the formation variation, which covers the unconfined compressive strength (UCS), confined compressive strength (CCS) abrasion, impact simulations, layer correlations, statistical analysis and contour mappings of interest zones. This paper will summarize the field history, delineate the bit design lineage in this long-term project, and then mainly focus on geology simulations. The objective of this paper is bring to light the importance of CCS simulations to predict the bit performance and help the bit design and selection; provide a bit design lineage and bit optimization workflow for the drilling operation to optimize the inventory utilization and streamline the decision-making loop; provide a case study with coordinating multiple disciplinary teams to achieve specified objective; and provide a concept of integration of geology and engineering in the