Investigation of Time-Lapse Changes with DAS Borehole Data at the Brady Geothermal Field Using Deconvolution Interferometry

Distributed acoustic sensing (DAS) has great potential for monitoring natural-resource reservoirs and borehole conditions. However, the large volume of data and complicated wavefield add challenges to processing and interpretation. In this study, we demonstrate that seismic interferometry based on deconvolution is a convenient tool for analyzing this complicated wavefield. We also show the limitation of this technique, in that it still requires good coupling to extract the signal of interest. We extract coherent waves from the observation of a borehole DAS system at the Brady geothermal field in Nevada. The extracted waves are cable or casing ringing that reverberate within a depth interval. These ringing phenomena are frequently observed in the vertical borehole DAS data. The deconvolution method allows us to examine the wavefield at different boundary conditions and separate the direct waves and the multiples. With these benefits, we can interpret the wavefields using a simple 1D string model and monitor its temporal changes. The velocity of this wave varies with depth, observation time, temperature, and pressure. We find the velocity is sensitive to disturbances in the borehole related to increasing operation intensity. The velocity decreases with rising temperature. The reverberation can be decomposed into distinct vibration modes in the spectrum. We find that the wave is dispersive and the fundamental mode propagates with a large velocity. This interferometry method can be useful for monitoring borehole conditions or reservoir property changes using densely-sampled DAS data.

Evaluation of Mud Weight Using Safe Mud Window Concept Based on Well Log Data: A Case Study of Well OP-002 in the North Sumatra Basin Area, Indonesia

In the drilling operation of well OP-002 which is located in the North Sumatra Basin at a depth interval of 2887 - 3186 m occurred partial loss, and caving at a depth interval of 500 - 1650 m, where the drilling problem is caused by the use of inappropriate mud weight. Safe mud window analysis is carried out by processing well log data to build PPFG (Pore Pressure Fracture Gradient) and 1D Geomechanics model using several calculation methods. Furthermore, the results of the calculation of pore pressure and fracture gradient are validated with well test data from the well OP-002, so the safe mud window can be determined, and can be used as a basis in the analysis of the drilling problems that occur. The optimum mud weight can minimize wellbore instability, with a limit value that must be greater than the collapse pressure, but not exceeding the minimum insitu stress limit. From the results of the mud safe window analysis, it can be concluded that at a depth interval of 500 - 1650 m caving occurs, because the density value used is smaller than the shear failure gradient, and at a depth interval of 1619 - 2829 m, the density value used is greater than Shmin. To overcome this problem, a mud wight with a safe mud window concept is recommended, namely the selection of the optimum mud weight to be used must be greater than the pore pressure and shear failure gradient and does not exceed the minimum horizontal stress and fracture gradient values.

TALDYK EARTHQUAKE on November 17, 2015 with KR=14.1, Mw=5.5 (Kyrgyzstan)

Information on the earthquake with KR=14.1, which occurred in Kyrgyzstan on November 17, 2015, is presented. Its epicenter is related to the South Fergana zone of the Osh region, in which felt earthquakes with intensity up to I=8–9 occurred repeatedly. This event was named Taldyk according to the settlement nearest to the epicenter. The earthquake was accompanied by numerous aftershocks: for the first day, 189 events were registered, for the second – 196, for the third – 84. Most part of the aftershocks is localized within the depth interval of 12–13 km, which is practically equal to the depth of the main shock (h=13 km). The focal mechanism of the main shock has a reverse type with strike-slip components. No serious investigation of the consequences of this earthquake carried out. Some macroseismic data are received from field reports of the station operators. For a more complete analysis of the possible impact of this earthquake and, first of all, for the needs of the Ministry of Emergency Situations of Kyrgyzstan Republic, a map of theoretical isoseismals was created.

Quantifying dating uncertainties in layer-counted paleoclimate proxy archives

Paleoclimate proxy records have non-negligible uncertainties that arise from both the proxy measurement and the dating processes. Knowledge of the dating uncertainties is important for a rigorous propagation to further analyses; for example for identification and dating of stadial-interstadial transitions in Greenland ice core records during glacial intervals, for comparing the variability in different proxy archives, and for model-data comparisons in general. In this study we develop a statistical framework to quantify and propagate dating uncertainties in layer-counted proxy archives using the example of the Greenland Ice Core Chronology 2005 (GICC05). We express the number of layers per depth interval as the sum of a structured component that represents both underlying physical processes and biases in layer counting, described by a regression model, and a noise component that represents the fluctuations of the underlying physical processes, as well as unbiased counting errors. The age-depth relationship of the joint dating uncertainties can then be described by a multivariate Gaussian process from which realizations of the chronology can be sampled. We show how the effect of an unknown counting bias can be incorporated in our framework and present refined estimates of the occurrence times of Dansgaard-Oeschger events evidenced in Greenland ice cores together with a complete uncertainty quantification of these timings.

Hydrocarbon source rocks in Kazhdumi and Pabdeh formations—a quick outlook in Gachsaran oilfield, SW Iran

Geochemical study of Kazhdumi and Pabdeh Formations as potential source rocks in Gachsaran Oilfield demonstrates that the Kazhdumi Formation has a fair to good capability of hydrocarbon generation and predominately contains type II-III kerogen. On the other hand, the Pabdeh Formation has a poor to good petroleum potential and contains different kerogen types, including type II, type II-III, type III and even for one sample, type IV, indicating different depositional conditions for this formation. The geochemical log of the Kazhdumi Formation shows that there is a close correlation between different geological parameters as noticed prominently in well number 55, which suggests the more extensive the anoxic condition, the higher the petroleum potential is for Kazhdumi Formation. By contrast, a poor correlation between TOC and other Rock–Eval-derived parameters for the Pabdeh Formation at a depth of more than 2100 m may demonstrate the inert organic matter and mineral matrix effects at this depth interval. However, biomarkers show differences in lithology and depositional environment for the Kazhdumi Formation in well numbers 55 and 83. On the other hand, the Pabdeh Formation has a mixed lithology (carbonate-shale) deposited in a marine setting under suboxic–anoxic condition. Moreover, thermal maturity indicators suggest that Pabdeh and Kazhdumi Formations are immature and early mature, respectively.

Coupling Production Data with the DFN to Unravel Natural Fracture Networks in a Tight Naturally Fractured Jurassic Reservoir, Kuwait

The Jurassic Najmah-Sargelu of west Kuwait can be thought of as a "hybrid" between a conventional and an unconventional reservoir. These systems form an increasingly important resource for operators, but their performance is unpredictable because matrix permeability is in the micro-Darcy range and production depends on natural fractures. Success depends on how well the static models are aligned to the dynamic production, and the effectiveness of a fit-for-purpose multistage completion on project economics. In this work we present our lessons learnt in production modelling these reservoirs and the coupling between reservoir simulation and the discrete fracture network (DFN). Our reservoir models were constructed using a highly integrated approach incorporating data from all scales and disciplines (drilling, geophysical, geological, reservoir and production) and the production simulations were run using dual porosity and black oil models. As expected, the DFN played a key part of this effort. An iterative approach was used to adjust the DFN so that it was consistent with production observations. However, in all cases care was made to ensure the new DFN honoured the seismic, geological, well log and drilling data from which it was generated. Final, smaller adjustments were made to the simulation model at the log scale to match PLT data. We used uncertainty analysis to run hundreds of simulation cases and found that the character of the natural fractures is quite well imprinted in the observed production data, particularly pressure buildup data. This gave us a better understanding of whether the natural fractures are diffuse and laterally extensive away from the wellbore or if they are localized close to the wellbore. Where reservoir simulation history matches inferred laterally extensive natural fractures, an good correlation was obtained with the natural fracturing from the DFN. This correlation was poor where natural fracturing was confined to a smaller depth interval (as observed from PLT), and is a result of the limitation in seismic resolution to resolve these natural fractures. The lessons learnt from our work helps towards improved understanding of production mechanisms of these reservoirs and their natural fracture networks. This, together with higher resolution azimuthal seismic, advanced wellbore characterization data and multistage completions are the desired key ingredients for technically enhancing production in these reservoirs.

Using Supervised Machine Learning Algorithms for Automated Lithology Prediction from Wireline Log Data

This paper presents advancements in machine learning and cloud deployment that enable rapid and accurate automated lithology interpretation. A supervised machine learning technique is described that enables rapid, consistent, and accurate lithology prediction alongside quantitative uncertainty from large wireline or logging-while-drilling (LWD) datasets. To leverage supervised machine learning, a team of geoscientists and petrophysicists made detailed lithology interpretations of wells to generate a comprehensive training dataset. Lithology interpretations were based on applying determinist cross-plotting by utilizing and combining various raw logs. This training dataset was used to develop a model and test a machine learning pipeline. The pipeline was applied to a dataset previously unseen by the algorithm, to predict lithology. A quality checking process was performed by a petrophysicist to validate new predictions delivered by the pipeline against human interpretations. Confidence in the interpretations was assessed in two ways. The prior probability was calculated, a measure of confidence in the input data being recognized by the model. Posterior probability was calculated, which quantifies the likelihood that a specified depth interval comprises a given lithology. The supervised machine learning algorithm ensured that the wells were interpreted consistently by removing interpreter biases and inconsistencies. The scalability of cloud computing enabled a large log dataset to be interpreted rapidly; >100 wells were interpreted consistently in five minutes, yielding >70% lithological match to the human petrophysical interpretation. Supervised machine learning methods have strong potential for classifying lithology from log data because: 1) they can automatically define complex, non-parametric, multi-variate relationships across several input logs; and 2) they allow classifications to be quantified confidently. Furthermore, this approach captured the knowledge and nuances of an interpreter's decisions by training the algorithm using human-interpreted labels. In the hydrocarbon industry, the quantity of generated data is predicted to increase by >300% between 2018 and 2023 (IDC, Worldwide Global DataSphere Forecast, 2019–2023). Additionally, the industry holds vast legacy data. This supervised machine learning approach can unlock the potential of some of these datasets by providing consistent lithology interpretations rapidly, allowing resources to be used more effectively.

Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning

The High Efficiency Video Coding Standard (HEVC) is one of the most advanced coding schemes at present, and its excellent coding performance is highly suitable for application in the navigation field with limited bandwidth. In recent years, the development of emerging technologies such as screen sharing and remote control has promoted the process of realizing the virtual driving of unmanned ships. In order to improve the transmission and coding efficiency during screen sharing, HEVC proposes a new extension scheme for screen content coding (HEVC-SCC), which is based on the original coding framework. SCC has improved the performance of compressing computer graphics content and video by adding new coding tools, but the complexity of the algorithm has also increased. At present, there is no delay in the compression optimization method designed for radar digital video in the field of navigation. Therefore, our paper starts from the perspective of increasing the speed of encoded radar video, and takes reducing the computational complexity of the rate distortion cost (RD-cost) as the goal of optimization. By analyzing the characteristics of shipborne radar digital video, a fast encoding algorithm for shipborne radar digital video based on deep learning is proposed. Firstly, a coding tree unit (CTU) division depth interval dataset of shipborne radar images was established. Secondly, in order to avoid erroneously skipping of the intra block copy (IBC)/palette mode (PLT) in the coding unit (CU) division search process, we designed a method to divide the depth interval by predicting the CTU in advance and limiting the CU rate distortion cost to be outside the traversal calculation depth interval, which effectively reduced the compression time. The effect of radar transmission and display shows that, within the acceptable range of Bjøntegaard Delta Bit Rate (BD-BR) and Bjøntegaard Delta Peak Signal to Noise Rate (BD-PSNR) attenuation, the algorithm proposed in this paper reduces the coding time by about 39.84%, on average, compared to SCM8.7.

Population dynamics and reproduction strategies of planktonic foraminifera in the open ocean

It has long been assumed that the population dynamics of planktonic foraminifera is characterised by synchronous reproduction associated with ontogenetic vertical migration. However, due to contradictory observations, this concept became controversial, and subsequent studies provided evidence both in favour and against these phenomena. Here we present new observations from replicated vertically resolved profiles of abundance and shell size variation in four species of planktonic foraminifera from the tropical Atlantic to test for the presence, pattern, and extent of synchronised reproduction and ontogenetic vertical migration in this oceanic region. Specimens of Globigerinita glutinata, Globigerinoides ruber ruber, Globorotalia menardii and Orbulina universa were collected over the first 700 m resolved at nine depth intervals at nine stations over a period of 14 d. Dead specimens were systematically observed irrespective of the depth interval, sampling day and size. Conversely, specimens in the smaller size fractions dominated the sampled populations at all times and were recorded at all depths, indicating that reproduction might have occurred continuously and throughout the occupied part of the water column. However, a closer look at the vertical and temporal size distribution of specimens within each species revealed an overrepresentation of large specimens in depths at the beginning of the sampling (shortly after the full moon) and an overrepresentation of small individuals at the surface and subsurface by the end of the sampling (around new moon). These observations imply that a disproportionately large portion of the population followed for each species a canonical reproductive trajectory, which involved synchronised reproduction and ontogenetic vertical migration with the descent of progressively maturing individuals. This concept is consistent with the initial observations from the Red Sea, on which the reproductive dynamics of planktonic foraminifera has been modelled. Our data extend this model to non-spinose and microperforate symbiont-bearing species, but contrary to the extension of the initial observations on other species of foraminifera, we cannot provide evidence for ontogenetic vertical migration with ascent during maturation. We also show that more than half of the population does not follow the canonical trajectory, which helps to reconcile the existing contrasting observations. Our results imply that the flux of empty shells of planktonic foraminifera in the open ocean should be pulsed, with disproportionately large amounts of disproportionately large specimens being delivered in pulses caused by synchronised reproduction. The presence of a large population reproducing outside of the canonical trajectory implies that individual foraminifera in a fossil sample will record in the calcite of their shells a range of habitat trajectories, with the canonical trajectory emerging statistically from a substantial background range.

NMR AND WELL LOGS PETROPHYSICAL CHARACTERIZATION OF SANDSTONE FROM THE MARACANGALHA FORMATION, BAHIA, BRAZIL

The storage and production capacity of reservoir rocks can be estimated through some petrophysics characteristics involving the lithological identification of the constitute rocks, fluids nature in the porous space, porosity, permeability, saturation and clay content. The most popular tools for obtaining these petrophysical parameters are the conventional geophysical well logs. However, the determination of petrophysical parameters from tools based on the phenomenon of nuclear magnetic resonance (NMR) has gained prominence in recent decades. In this work, we analyzed rock samples from outcrops in Frades Island region, Bahia, Brazil, through laboratory NMR measurements, to estimate and evaluate the petrophysical properties of the Maracangalha Formation, one of the main hydrocarbons reservoirs in the Recôncavo Basin. The Sandstone samples were characterized in terms of porosity, permeability, saturation, and petrofacies. Finally, we calculated porosity, permeability, and clay content using data from gamma-ray, electrical and density logs, measured in a depth interval interpreted for Maracangalha Formation. These results corroborate with the obtained by NMR since, despite the effects of weathering and erosion on the samples used, the values of porosity and permeability obtained in NMR are in the range of values calculated from these profiles.