Log Acquisition Research Articles

A Method for Solving Problems in Acquiring Communication Logs on End Hosts

In the process of collecting evidence of activities and events in network devices, there are problems with content and storage, and we aim to solve the problems faced by network devices in network forensics. In this paper, we propose a simple method for solving the problems with content and storage in acquiring communication logs on end hosts, implement a sniffing tool that captures raw packets with communication event control, compare it with existing tools, and conduct experiments and considerations. Through these experiments and considerations, we confirmed that the proposed communication log acquisition method can be implemented on the end host, and that the problem can be solved by using a tool that implements the proposed method. Also, we confirmed that it can be applied to real-world communication log collection scenarios, and that it can coexist with existing systems and tools that collect communication logs.

Treatment readiness and prognosis for problematic smartphone use: Evaluation of the Stages of Change, Readiness, and Treatment Eagerness Scale (SOCRATES) and log data.

While moderate smartphone use contributes to information gathering and relationship building, excessive smartphone use, also referred to as problematic smartphone use (PSU), has raised concerns because of its addictive nature and associated health consequences. This study aimed to investigate the relationship between treatment readiness and prognosis in individuals with PSU and to assess the predictive ability of smartphone log data in evaluating treatment readiness. A sample of 47 patients with PSU participated in this study. Treatment readiness was assessed using the Stages of Change, Readiness, and Treatment Eagerness Scale (SOCRATES), and log data were collected using a smartphone log application. The results showed a significant correlation between baseline SOCRATES scores and the difference in Global Assessment of Functioning scores between baseline and 6 months (Spearman's ρ = 0.640, P-value = .001), suggesting that treatment readiness may explain part of the treatment outcomes (Pearson's r 2 = 0.379, P-value = 0.032). In addition, baseline log data, including the log acquisition rate, showed a positive correlation with treatment readiness (Spearman's ρ = 0.328, P-value = 0.045). These findings provide valuable insights into the relationship between treatment readiness and clinical outcomes in patients with PSU, and suggest the potential of log data as objective indicators of treatment motivation.

Estimation of Petrophysical Parameters of Carbonates Based on Well Logs and Laboratory Measurements, a Review

The purpose of this review paper is to show the possibilities of carbonate reservoir characterization using well logging and laboratory measurements. Attention was focused on standard and new methods of well logging acquisition and interpretation including laboratory experiments to show a part of the history of carbonate rock investigations as hydrocarbon or water reservoirs. Brief information on the geology, mineralogy and petrography of carbonate rocks was delivered. Reservoir properties, i.e., porosity (including fracturing), permeability, and saturation, were defined to emphasize the specific features of carbonates, such as fractures, and vugs. Examples of methodologies were selected from the commonly used laboratory techniques (thin sections examination, mercury and helium porosimetry, X-ray diffraction—XRD) combined with the standard well logs (bulk density—RHOB, neutron porosity—NPHI, sonic slowness—DT, and deep resistivity—Rd) to show the methods that have been used since the very beginning of the scientific and engineering studies of carbonates. Novelty in well logging, i.e., resistivity and acoustic imaging, nuclear magnetic resonance–NMR, dipole shear sonic imaging–DSI, and a spectral neutron-gamma log-geochemical device–GLT combined with modern laboratory investigations (NMR laboratory experiments, scanning electron microscopy SEM), showed how continuous information on mineral composition, porosity and saturation could be obtained and juxtaposed with very detailed laboratory data. Computed X-ray tomography (CT) enabling the 2D and 3D analyses of pores and fractures was presented as a quantitative methodology, effective in pore space characterization, revealing rock filtration abilities. Deep learning and artificial intelligence were used for joining various types of data. It was shown that thanks to new computational technologies original data from very small samples (micro scale), extensively describing the flow ability of the reservoir, could be extended to mezzo scale (core samples) and macro scale (well log images). Selected examples from the published papers illustrated the review. References cited in the text, together with the issues included in them, were the rich source of the practical knowledge processed These were checked by the authors and could be used in other projects.

Geothermal Gradients in the Upper Amazon Basin derived from BHT data

The Upper Amazon Basin (UAB), present foredeep of the sub Andean retro-foreland basin. It comprises Putumayo area (southeastern part of Colombia), Oriente area (eastern Ecuador) and Marañón area (northeastern part of Peru). Bottom Hole Temperature (BHT) from a regional well log database (1076 wells and 2957 logs) were analyzed and data discriminated according to drilling operations (i.e., log acquisition operations, cementing, formation test, tools misreading values), topography and shallow subsurface weathering conditions (i.e., temperature data from wells with depths below 305 m. were avoided). A new normalized database has been setup (1021 wells and 1399 logs). Analysis of this data set has allowed better understanding of the regional distribution of geothermal gradient variations within the study area. The results indicate mean uncorrected geothermal gradient (UCGG) for the UAB of 20.4 °C/km. The UCGG is a first approach based on well data with sufficient information and is useful for comparison purposes with other basin where corrected data is limited. In addition, a new computer-generated contour Geothermal Gradient Map (GGM) has been created, using 56 locations (for which number of BHT at different depths ≥ 3, and temperatures in the range 23.4 to 44.4 °C). As for the locations 2 are in Colombia, 28 in Ecuador and 26 in Peru. This map is useful in analysis data of UCGG due to its wide distribution along the basin. Finally, correction based on Horner´s method was applied to these datasets (where number of BHT ≥3 at the same depth; time since circulation - TSC incremental), obtaining a Corrected Geothermal Gradient (HCGG) of 22.9 °C/km (46 wells and 153 logs). We recommend the use of this gradient for comparative reference purposes.

Cased-Hole Standalone Evaluation Succeeds in the Absence of Openhole Data

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 202894, “Cased Hole Standalone Evaluation: Breaking the Barrier To Successfully Evaluate Challenging Deep Carbonate Reservoirs,” by Pradeep Menon and Carey Mills, ADNOC, and Suvodip Dasgupta, SPE, Schlumberger, et al., prepared for the 2020 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, held virtually from 9-12 November. The paper has not been peer reviewed. Accurate petrophysical evaluations (formation lithology, porosity, and water saturation) are essential in characterizing potential reservoir zones and estimating resources in place. Typically, these evaluations rely on acquisition of openhole logging measurements; however, this is not always possible. The complete paper outlines two examples from tight gas reservoirs in two separate fields offshore Abu Dhabi in which openhole data could not be acquired and petrophysical analysis was undertaken using cased-hole log data. These evaluations successfully identified gas-saturated porous intervals in each well, one of which was successfully flow-tested. Introduction A growing need exists to increase gas production in the UAE. As a result, specific gas-production targets have been mandated from development of currently undeveloped deep gas carbonate reservoirs such as the Permo-Triassic Khuff formation, the middle Jurassic Araej formation, and the Permian Pre-Khuff Unayzah and Berwarth formations. Recent appraisal wells have aimed at evaluating these reservoirs systematically by acquiring a good suite of openhole logs, cutting conventional cores, and conducting well-testing operations. These well data are combined with an evolving regional understanding to better assess and ultimately develop these complex formations. An accurate petrophysical evaluation requires the petrophysicist to develop a realistic evaluation of formation lithology, porosity, and water saturation. These parameters provide the foundations for further work such as static modeling stands, and they must be robust. The Upper Khuff is composed of dolomite occasionally grading to calcareous dolomite with minor interbeds of claystone and anhydrite. In core and cuttings, the dolomite in the uppermost section exhibits a grainstone texture with poor intercrystalline/intergranular porosity. The Lower Khuff is composed of very hard dolomite in part grading to calcareous dolomite, medium-to-dark grey-brown in places, with occasional very-fine-to-medium grainstone texture and very poor intercrystalline porosity. In this paper, the Upper and Lower Araej members are interpreted to have been deposited in an open, marine- circulation shelfal environment, while the Uweinat member is considered to have been deposited in a more- restricted circulation setting with-in a similar shelfal environment. The Barrier Openhole logging generally is the preference for formation evaluation because it represents the simplest environments and benefits from a comprehensive list of available measurements. The variety of tools and diversity of output data available make openhole log acquisition the gold standard for formation evaluation. However, in certain situations in which openhole logging is not possible because of borehole conditions (re-entry of old cased wells, wellbore instability, over-pressure), no option exists other than acquiring petrophysical data in a cased-hole environment.

Synthetic geochemical well logs generation using ensemble machine learning techniques for the Brazilian pre-salt reservoirs

Geochemical logs are an essential tool for hydrocarbon reservoir characterization. The rock composition given by these logs is useful for porosity calculation, stratigraphic modeling, diagenesis estimation, and well acidification. In reduced logging operations performed in carbonate reservoirs of the Brazilian pre-salt, the geochemical tool is no longer acquired, aiming at optimization and cost reduction. This research aims the development of synthetic geochemical logs using machine learning algorithms. The database includes 22 wells with complete logging acquisition, and the input logs are natural gamma-ray, gamma-ray spectroscopy, density, photoelectric factor, neutron porosity, nuclear magnetic resonance, and sonic. The chemical elements chosen as output are Al, Ca, Fe, Mg, Na, Si, S, and Ti. Five models based on machine learning are trained using 19 wells: Support-vector machine, Multilayer perceptron, Random Forest, AdaBoost, and XGBoost. AdaBoost represents the best algorithm because, in addition to showing the best results, it allowed for a more simplified preprocessing and hyperparameter tuning. The evaluation of the models applies R2 and root mean squared error (RMSE) in validation data and cross-validation. Robust models are acquired for Al, Ca, Fe, Mg, Si, S, and Ti, with R2 above 0.80. Na shows R2 slightly above 0.70. All the models have RMSE between 10-2 to 10-4. The most important logs during training are density, photoelectric factor, K, Th, U, and neutron porosity. Synthetic geochemical logs created for three test wells show a good agreement with acquired logs, being able to reproduce general trends of the pre-salt formations. The machine learning model is capable of substituting the acquisition of geochemical logs with high confidence, representing cost reduction, and supplying engineers and geoscientists with quality data to be used in formation evaluation.

Linkages between hydrothermal alteration, natural fractures, and permeability: Integration of borehole data for reservoir characterization at the Fallon FORGE EGS site, Nevada, USA

Between 2015 and 2018, the Fallon FORGE EGS (Engineered or Enhanced Geothermal System) site in Churchill County, Nevada, USA, underwent detailed geoscientific characterization to evaluate its suitability to operate as a field demonstration site to test and develop EGS technologies. This included the compilation, synthesis and acquisition of geophysical, geological, geochemical, hydrological, down-hole log, and mineralogy datasets. Site activities culminated in the drilling of a 2481 m-deep EGS observation well (21−31) to further verify the geology and rock properties of the proposed EGS reservoir formations, which are older than the Miocene and include felsic intrusions, felsic volcanics, and meta-sedimentary lithologies. In this paper, we integrate and evaluate borehole data from well 21−31, including drill cuttings and sidewall cores (petrographic, XRD, hyperspectral), wireline geophysical logs (e.g., gamma, neutron porosity, resistivity, sonic scanner), borehole imagers (FMI, BHTV), and pressure and temperature logs. Hydrothermal alteration and veining is found throughout the entire pre-Miocene interval of well 21−31, and includes epidote mineral assemblages that suggest past temperatures >250 °C. This is higher than the hottest measured equilibrated temperature at similar depths in other wells on the FORGE site today, and suggests that the system was higher temperature in the past. Overall lithologic variability is high, with volcanic, intrusive, metamorphic, and sedimentary rocks present in the bottom 700 m of the well. Natural fractures are abundant throughout the pre-Miocene section, but are more frequent in the felsic intrusive and felsic volcanic lithologies. The dominant orientation of these natural fractures observed in FMI logs is NNE-SSW with an average strike of 010° ± 40°, subparallel to the interpreted SHmax orientation of 009° ± 14° that is indicated by drilling-induced structures in the wellbore in the BHTV log. Therefore, natural fractures appear optimally oriented for reactivation via hydraulic stimulation. Mud loss locations and a non-equilibrated temperature log suggest that there are some zones with relatively-higher permeability and/or open fractures. However injection test results indicate an injectivity index of 0.025 L/min/kPa, which is an order of magnitude lower than a typical threshold for commercial wells. Analytical modeling of injection test results indicate that the majority of the well is low permeability (< 10−16 m²), with some discrete, localized zones of higher permeability (∼ 10 -12 m², less than 0.5 percent of the pre-Miocene (deep) section intersected by the well).

Research and system design and implementation of log - based computer forensics technology

This thesis is in the “large scale research of Internet information security system” of the background, studied the computer log network forensics technology, focuses on the log forensics analysis and integrity protection and authentication technology, designed and implemented based on computer network system log user behavior forensic analysis system. This paper summarizes the relevant concepts of computer forensics technology, electronic evidence and system log, demonstrates the feasibility of log as electronic evidence, and proposes the realization model of computer network forensics based on system log, and analyzes the problems that should be paid attention to in computer log forensics. This paper studies the key issues in the process of log forensics, including the collection, transmission, preservation, analysis and submission of logs, in which the analysis of log forensics and integrity protection and verification are the focus of the research. Forensic analysis of logs is to mine log records related to intrusion events by analyzing massive log data, which is divided into statistical analysis and correlation analysis. Paper presents the overall structure of the system is designed, and the subsystem module function and working process of description, implements a user behavior based on log computer network forensics analysis system, and from the safety of the system, the log acquisition performance, the integrity of the data transmission performance, the log data, forensic analysis and so on five aspects of the performance of the system were tested and analyzed, the analysis results show that the system has the basic function of the network forensics.

Research on the Security Early Warning Model of Campus Network Based on Log

The safe and stable operation of campus network is core problems which network administrators focused on. This paper takes safety management of campus network as a main research object to analyze the weakness in safety management and threat management of campus network, which can put forward safety warning of campus network that based on log. What’s more, it also analyzes the protocol standard and related technologies of log acquisition, studies the log acquisition, threat assessment, security warning in the model to provide the core process for system implementation. This model can quickly find out and timely cope with the network safety threats, it also plays positive guiding roles in improving the safety and stability of network operation and reducing the workload of network management.

Long Short-Term Memory and Variational Autoencoder With Convolutional Neural Networks for Generating NMR T2 Distributions

Downhole nuclear magnetic resonance (NMR) logs acquired in the borehole environment are valuable for subsurface characterization because they contain information about the pore size distribution, fluid composition, fluid saturation, fluid mobility, formation permeability, and porosity. NMR log acquisition can be challenging due to operational and financial constraints. Recently, NMR T2 distributions of the subsurface were generated by processing conventional well logs using deeplearning neural-network (NN) models. This improves the accessibility to subsurface pore size distributions. We implement two neural-network models, variational autoencoder-based NN with convolutional layers and long short-term memory (LSTM), to generate NMR T2 distributions from formation mineral content and fluid saturation logs. Prediction performance is evaluated for the entire NMR T2 spectrum ranging from 0.3 to 3000 ms as well as for T2 spectra within four bins obtained by dividing the entire spectrum into four equal parts. Each bin represents a specific pore size range. In terms of R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , both the models have prediction performances above R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.75 for the entire spectrum. The best prediction performance is achieved for the bin of size ranging from 2.7 to 28 ms, representing pore diameters from 10 to 100 nm. The performance of this bin in terms of R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> is 0.78. The LSTM model is highly sensitive to noise in T2 distribution used during the training, and both the models are robust to noise in the conventional input logs.

Machine learning technique for the prediction of shear wave velocity using petrophysical logs

Compressional wave velocity (Vp) and shear wave velocity (Vs) are widely used as quick, easy to use, and cost-effective means of determining the mechanical properties of formations in the petroleum industry. However, shear wave logs are only available in a limited number of wells in an oil field due to the high cost of the log acquisition. For this reason, many attempts have been made to find a correlation between Vs and other petrophysical logs. In this study, a set of log data consisting of depth, neutron porosity (NPHI), density (RHOB), photoelectric (PEF), gamma ray (GR), caliper, true resistivity (RT), VP, and Vs were used to develop a model for Vs estimation in a well drilled in Ahvaz field. For this purpose, Tukey's method was employed to preprocess the data and eliminate outliers. Then, using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the best features were selected among the inputs to estimate Vs. Results indicate that increasing the number of input parameters of the model leads to an increase in accuracy and determination coefficient; however, this increase was negligible for the models with more than five input parameters. These parameters, i.e., Vp, RT, GR, RHOB, and NPHI, were selected as the best features to estimate Vs adopting Least Square Support Vector Machine (LSSVM) combined with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Cuckoo Optimization Algorithm (COA). Results obtained from modeling reveal that LSSVM-COA algorithm produces a more accurate estimate of VS compared to LSSVM-PSO and LSSVM-COA in both training and testing steps. In addition, a very slight difference between the error in testing and training steps suggests a higher reliability of LSSVM-COA compared to two other hybrid algorithms. Comparing this model with empirical and regression ones clearly showed that the model error is considerably less than those models. Eventually, another set of data gathered in Ab-Teymour field was used to evaluate the model from the perspective of generalizability. Similarly, Vs estimation by LSSVM-COA model provided more reliable and accurate results compared to those of empirical and regression models, and also LSSVM-PSO and LSSVM-GA algorithms. Finally, it can be concluded that the method employed in this study could be used as an efficient means to accurately estimate Vs.

A natural gas hydrate system on the Exmouth Plateau (NW shelf of Australia) sourced by thermogenic hydrocarbon leakage

Abstract The identification of natural gas hydrates and shallow free gas in sedimentary basins is critical for understanding the organic carbon cycle dynamics in the shallow geosphere, as well as for geohazard studies related to the development of commercial hydrocarbon fields. In this study, we report for the first time the evidence for the potential occurrence of a natural gas hydrate system along the continental margin of Australia, on the Exmouth Plateau (NW Shelf). By the use of high-quality 3D seismic data, calibrated by downhole data from ODP and industry boreholes, we interpret a series of shallow high-amplitude anomalies, including patchy bottom-simulating reflections, as the seismic expression of localised accumulations of free gas beneath the base of hydrate stability, and overlying high-saturation hydrates or authigenic carbonates. The hydrate-bearing reservoir is constituted by Neogene and Quaternary fine-grained carbonate nannofossil and foraminifera-rich oozes. The patchy distribution of the shallow free gas and hydrate accumulations, which reflects the geometry of deeper fault blocks, is hypothesised to result from the leakage of thermogenic gas from deeper reservoirs. The interpretation of a thermogenic origin for the hydrate-forming gases is supported by (1) the existence in the study area of several thermogenic gas discoveries within Late Triassic reservoirs, (2) seismic evidence of cross-stratal and stratal pathways for migrating gases in the overburden, (3) the presence of free gas accumulations at depths intermediate between the deep reservoirs and shallow gas hydrate systems, and (4) geochemical and lithological evidence that the conditions which favour the generation of shallow microbial gas are not present in the area. The acquisition of downhole log and sample data through the observed seismic amplitude anomalies is necessary to test the interpretations and hypotheses presented in this manuscript.

Application Research on Cloud Platform Forensics

The security of cloud computing has attracted the attention of an increasing number of people while providing convenience. The characteristics of cloud computing itself make the traditional forensics tools for forensics framework no longer applicable. This paper focuses on introducing the log acquisition forensic methods based on private cloud platform of OpenStack from the prospect of evidence obtaining. Then the existing work of cloud forensics field is analysed.

Synthetic core from conventional logs: A new method of interpretation for identification of key reservoir properties

We developed a unique method to generate reservoir attributes by creating an artificial core for those wells that have no core, but that have gamma, neutron, and density logs. We examined sedimentary facies distributions, reservoir attributes, and mechanical parameters of the rock for noncored wells to increase the data density and improve the understanding of the reservoir. This method eventually helps to improve high-resolution 3D geocellular models, geomechanical models, and reservoir simulation in reservoir characterization. Artificial or synthetic cores are created using a single curve that builds facies templates using the information from the cores of nearby offset wells, which belong to the same depositional environment. The single curve, called the fine particle volume (FPV), is the average of two shale volumes calculated from the gamma-ray log and from a combination of neutron and density logs. Using facies templates, the FPV curve builds the synthetic core for geocellular modeling and reservoir simulation, and it represents the sedimentary facies distribution in the well with all the reservoir attributes obtained from laboratory data of the original core. The vertical succession of the synthetic core has the characteristics of actual sedimentary facies with reservoir attributes such as porosity, permeability, and other rock properties. The result of creating the synthetic core was validated visually and statistically with the actual cores, and each of the cored wells was considered as a noncored well. The limitation of this method is associated with the accuracy of the logging data acquisition, normalization factors, and facies template selection criteria.

Analysis Engine Based on Cloud Computing

In this paper come up with the idea of user behavior analysis engine, the combination of the static analysis of user behavior and real-time monitoring, real-time acquisition of Web log and user to access the context information of the page, apply to the improved data mining model analysis, which based on cloud computing technology, meanwhile efficient processing and storage, cloud database test showed that, the system can significantly improve the effect and efficiency of user behavior analysis.

The Design of Well Logging Data Acquisition Based on FPGA

A novel kind of logging data acquisition is designed based on FPGA.In this paper, the hardware and software are described in detail. Experiment results show that the designed circuit and program are reasonable and reliable by the logging signal simulator, which can meet the logging requirement of oil field.

Network-Based Resource-Proximity Analysis of Primary Wood Processing Mills in Arkansas

Abstract Primary wood processing mills in Arkansas play a vital role in both the state and the national economy, as evidenced by Arkansas' high national ranking in lumber productivity. Log acquisition from forestlands is invariably constrained by suitability of terrain and road networks; hence, an accurate assessment of the sufficiency of timberlands in servicing mills based on the existing road network and cost-effective log-truck travel time is essential for planning for the future. Many different analyses were carried out on an ArcInfo 9.3.1 workstation to delineate cost-effective sawmill service areas (SSA), timber supply areas (TSA), agricultural lands, and the overlaps that exist between these land patches. Zonal area summation of the land patches was analyzed with a two-sample paired t-test. Results indicated that there were significant pairwise differences (P &lt; 0.0001) in the extent of SSA and TSA, SSA and SSA within TSA, SSA without agricultural lands and SSA within TSA, agricultural lands with SSA, and agricultural lands without SSA. This study indicated that a significant portion (10%) of agricultural lands must be used for optimal stocking of the delineated cost-effective SSA. Furthermore, this study revealed the suitable areas in Arkansas where there are clusters of residual timberlands that can serve as a raw material supply base for new mills.

Technology Focus: Formation Evaluation (August 2010)

Technology Focus Analysts of conventional logs, who routinely evaluate complex formations, may be less productive when faced with more-esoteric responses from modern tools, especially because the latter often are interpreted by the logging contractors themselves. This situation arises because available petrophysical software may not be capable of handling the new log responses, and the required interpretive algorithms often are claimed as proprietary and, therefore, are withheld. Logging-contractor-generated results from new logs may bring additional insight that is not readily apparent from basic logging suites, but end users must beware of blindly accepting these results and the implication that the new tools are necessary. Many published case studies that claim new logging services are essential do not prove that basic logs are inadequate. Readers should be skeptical if these conference papers do not conform to Richard Feynman’s exhortation, “The idea is to try and give all the information to help others to judge the value of your contribution, not just the information that leads to judgment in one particular direction or another.” We should always ask ourselves three questions: Was the log really necessary? Does it add new information or simply confirm what could have been determined from a basic logging suite? For example, a nuclear-magnetic-resonance log in shaly gas-bearing sands with oil-based-mud-filtrate invasion may be able to distinguish gas from oil, but invaded-zone fluids also can be solved for by use of a combination of conventional logs. Is the logging-contractor interpretation correct? New logs should be validated against careful traditional-log analysis, which has been calibrated against core. Can operators improve on the logging-contractor interpretation? Yes! Operators have more relevant data and local knowledge, so they can use the log data more effectively. Petrophysical software and a log analyst’s time cost much less than log acquisition. Logging contractors should run a safe and efficient operation while acquiring good raw data. If they also can supply an interpretation, then this is a bonus. End users must not abdicate their responsibility for the interpretation of log data that their employer pays to acquire. Formation Evaluation additional reading available at OnePetro: www.onepetro.org SPE 125342 • “Reservoir-Rock-Types Application—Kashagan” by A. Francesconi, Eni, et al. SPE 128013 • “Residual Hydrocarbons—A Trap for the Unwary” by T. O’Sullivan, SPE, Cairn India, et al. SPE 130414 • “Fulmar Sandstones—Correcting Some Received Wisdom in the Greater Kittiwake Area (North Sea)” by G. Coghlan, SPE, Centrica Energy, et al.

Seabed logging acquisition as a tool in exploration decision-making

For medium to larger-sized oil companies, decreasing production has made production well drilling, rather than exploration wells, a priority. Regulations have historically been strict for smaller energy companies that wanted to prequalify as licensed partners in fields offshore Norway. However, different regulations and changes in the fiscal policy have made it possible for a number of new companies, Norwegian and foreign, to enter the Norwegian sector over the last couple of years.

Prolific deep-marine slope channels of the Nile Delta, Egypt

The Nile Delta offshore is rapidly emerging as a major gas province. High-quality three-dimensional (3-D) seismic data, coupled with data from 13 consecutive successful deep-water exploration and appraisal wells, have highlighted clear phases of erosion and deposition within the upper Pliocene deep-marine slope channels. The gross reservoir architecture is spectacularly imaged by 3-D seismic techniques, both in time sections and through a variety of amplitude extractions, while an extensive program of core and wire-line log acquisition and analysis has enabled high-resolution definition of the channel-fill sediments. The channels were initiated by the introduction of coarse sediments to the shelf edge possibly at times of relative sea level fall. Initially, there was significant erosion, especially in areas up depositional dip, creating what we term slope valleys. Subsequent valley infill commonly commenced with debris flows, slumps, and slides, sometimes overlying basal, bypass-related sands, and progressed to amalgamated or stacked channels in packages of upward-decreasing net-to-gross sand ratios. This pattern was commonly repeated following reincision, which may have occurred several times. The different stages of channel development can be considered in terms of slope equilibrium with a reduction in slope gradient promoted by increases in flow size and density and decreases in grain size.