Log Analysis Techniques Research Articles

A framework for detection and mitigation of cyber criminal activities using university networks in Kazakhstan

Abstract. The increasing number of information security incidents in higher education underscores the urgent need for robust cybersecurity measures. This paper proposes a comprehensive framework designed to analyze the illegal use of internet resources in university networks in Kazakhstan. The subject of this article is the detection and mitigation of cybercriminal activities using university networks in Kazakhstan. The goal is to develop a comprehensive framework that integrates multiple educational organizations to enhance collaborative security efforts by monitoring network activity and categorizing texts using machine learning techniques. The tasks to be solved are: to formalize the procedure of integrating multiple educational organizations into a collaborative cybersecurity framework; developing a log analysis tool tailored for monitoring network activities within university networks; creating a novel dictionary of extremist terms in the Kazakh language for text categorization; to implement advanced machine learning models for network traffic classification. The methods used are: log analysis tools for real-time monitoring and anomaly detection in network activities, Natural language processing (NLP) techniques to develop a specialized dictionary of extremist terms in Kazakh, Machine learning models to classify network traffic and detect potential cyber threats, and collaborative architecture design to integrate network security efforts across multiple institutions. The following results were obtained: a comprehensive log analysis tool was developed and implemented, providing real-time monitoring of network activities in university networks; a dictionary of extremist terms in Kazakh was created, facilitating the categorization and analysis of texts related to potential security threats; advanced machine learning models were successfully applied to classify network traffic, enhancing the detection and mitigation of cyber threats; and an experimental architecture integrating multiple educational organizations was established, fostering collaborative efforts in cybersecurity. Conclusions. The scientific novelty of the results obtained is as follows: 1) a robust framework for collaborative cybersecurity in educational institutions was developed, leveraging log analysis and machine learning techniques; 2) the creation of a specialized dictionary of extremist terms in Kazakh significantly improved the accuracy of text categorization related to cybersecurity; 3) the application of advanced machine learning models to network traffic classification provided a methodological approach to effectively managing and securing network infrastructure effectively; 4) the experimental architecture demonstrated the potential for enhanced security through collaboration among educational organizations, offering strategic recommendations for improving information security in academic environments. The outcomes of this research contribute to the broader cybersecurity field by providing a structured approach to detecting and mitigating cyber threats in educational contexts. The proposed framework has potential applications extending to global security frameworks, aiming to foster a safer internet usage environment and reduce the risks associated with cyber threats and unauthorized data access.

Well Log Analysis and Comparison of Supervised Machine Learning Algorithms for Lithofacies Identification in Pab Formation, Lower Indus Basin

Machine learning uses higher-order statistical computation for model prediction by combining statistics with advanced algorithms. These predictions can be used to infer the characteristics of potential hydrocarbon reservoirs using well log data. The objective of this study is to predict and segregate the potential reservoir and non-reservoir facies in the Cretaceous Pab Formation based on well log analysis and machine learning techniques using the borehole data of three wells drilled in the Lower Indus Basin. The Petrophysical interpretation has been performed to mark the potential reservoir facies, namely, gas sand, wet sand, and shale using cutoff values for shale volume (Vshale) and water saturation (SW). Before any further analysis, the quality of the well log data for the wells was checked by using heat maps and box plots to identify the outliers in the available dataset. The important available featured logs were used in the Random Forest (RF) and the Decision Tree (DT) algorithms to predict the facies in each well by training the other two wells. The prediction accuracy score between the petrophysical and the predicted potential reservoir facies using RF classifier shows 92 to 94% accuracy, whereas prediction accuracy score of DT classifier is 80 to 85%. The comparison of the two techniques indicates that the predictions using RF algorithm are relatively more accurate therefore making it more efficient for predicting the potential reservoir facies in the wells where the sophisticated well logs are unavailable.

Bayesian Network analysis of software logs for data‐driven software maintenance

AbstractSoftware organisations aim to develop and maintain high‐quality software systems. Due to large amounts of behaviour data available, software organisations can conduct data‐driven software maintenance. Indeed, software quality assurance and improvement programs have attracted many researchers' attention. Bayesian Networks (BNs) are proposed as a log analysis technique to discover poor performance indicators in a system and to explore usage patterns that usually require temporal analysis. For this, an action research study is designed and conducted to improve the software quality and the user experience of a web application using BNs as a technique to analyse software logs. To this aim, three models with BNs are created. As a result, multiple enhancement points have been identified within the application ranging from performance issues and errors to recurring user usage patterns. These enhancement points enable the creation of cards in the Scrum process of the web application, contributing to its data‐driven software maintenance. Finally, the authors consider that BNs within quality‐aware and data‐driven software maintenance have great potential as a software log analysis technique and encourage the community to deepen its possible applications. For this, the applied methodology and a replication package are shared.

Survey on Online Log Parsers

Technological dependence is growing in leaps and bounds as days progress. As a result, software applications are required to be up and running at all times without fail. The health and safety of these applications need to be monitored regularly by theuse of constant logging of any faults that occur at their runtime executions. Log analysis techniques are applied to recorded logsto obtain a better overview of how to handle failures and health deterioration. Before these algorithms can be utilized in practice, the raw unstructured logs need to be converted into structured log events. This process is performed by log parsers, which are accessible in two different modes – offline and online. While offline log parsers have a predefined knowledge base containing templates and conversion rules, online log parsers learn new templates on the job. This paper focuses on surveying and creating a comparative study on online log parses by analysing the type of technique used, efficiency and accuracy of the parser on a given dataset, time complexity, and their effectiveness in motivating applications.

Examining the Relationship Between Occupational Physical Activity and Hypertension Status: Evidence from the Ghana WHO Study on Global Ageing and Adult Health.

The employment landscape has changed significantly over the past few years in emerging economies including Ghana where many people are rapidly transitioning from livelihood activities that originally involved physical exertion to work environments where performance of duties are mostly non-physical. Working under non-active conditions could, however, exacerbate the risk of developing chronic diseases that are increasingly becoming problematic in many developing countries. Drawing on a cross-sectional nationally representative sample data of 4425 adult Ghanaian workers collected by the WHO Study on Global Ageing and Adult Health in developing countries, we examined the relationship between occupational physical activity and hypertensive status. We employed the complementary log-log analysis technique to build nested models with results presented in odds ratios. After controlling for several relevant variables, the results show that performing sedentary related work (AOR = 1.23, CI = 1.06-1.42) is significantly associated with a higher likelihood of being hypertensive compared with those whose work involved moderate physical activity. Other factors that were significantly associated with being hypertensive included tobacco use (AOR = 1.33, CI = 1.05-1.70), living in an urban environment (AOR = 1.15, CI = 1.01-1.32), and being a female (AOR = 1.18, CI = 1.01-1.37) and being an older person. Policies on reducing the risk of developing chronic conditions especially hypertension need to recognize the contributions of the work environment in emerging developing economies.

Log-based software monitoring: a systematic mapping study

Modern software development and operations rely on monitoring to understand how systems behave in production. The data provided by application logs and runtime environment are essential to detect and diagnose undesired behavior and improve system reliability. However, despite the rich ecosystem around industry-ready log solutions, monitoring complex systems and getting insights from log data remains a challenge. Researchers and practitioners have been actively working to address several challenges related to logs, e.g., how to effectively provide better tooling support for logging decisions to developers, how to effectively process and store log data, and how to extract insights from log data. A holistic view of the research effort on logging practices and automated log analysis is key to provide directions and disseminate the state-of-the-art for technology transfer. In this paper, we study 108 papers (72 research track papers, 24 journals, and 12 industry track papers) from different communities (e.g., machine learning, software engineering, and systems) and structure the research field in light of the life-cycle of log data. Our analysis shows that (1) logging is challenging not only in open-source projects but also in industry, (2) machine learning is a promising approach to enable a contextual analysis of source code for log recommendation but further investigation is required to assess the usability of those tools in practice, (3) few studies approached efficient persistence of log data, and (4) there are open opportunities to analyze application logs and to evaluate state-of-the-art log analysis techniques in a DevOps context.

Use of Inline Spinner for Determination of Zonal Flow Rates in Vertical and Moderately Deviated Wells

Summary Accurate zonal flow rate determination is necessary for better reservoir behavior understanding and for making important decisions that can improve well productivity. Knowledge of the capabilities of different reservoir zones in the same well also has significant importance in reservoir performance monitoring and selection of perforation intervals in development wells. Conventional production log analysis techniques can usually yield good results only if the fullbore spinner readings are reliable. However, the fullbore spinner measurement may not be available in some wells. Examples include cases in which the fullbore spinner cannot access the well due to mechanical obstruction, or when the casing is not clean enough, causing potential plugging of fullbore spinner blades. In these situations, the fullbore flow-rate readings may not be available or at least unclear or confusing, which may lead to incorrect decisions. In many of these situations, inline spinner (ILS) data may be readily available. The ILS is often used for qualitative interpretation (i.e., determining which zones are producing), but there is not a specific method to use the ILS for a quantitative solution in the absence of surface measurements of rates. In this paper, we introduce a new method to calculate the volumetric zonal flow rate using ILS data with high accuracy. Approximately 40 oil wells are used to develop an empirical correlation to compute zonal flow rates from ILS data in casing strings. The new method was used to quantitatively interpret eight oil wells for validation. In these wells, fullbore and ILS data were significantly different. The new method for interpretation of ILS data provided results consistent with surface production tests and led to decisions that contributed to increasing production rates.

Sampling business process event logs using graph‐based ranking model

SummaryModern information systems are continuously collecting and storing large volumes of business process event logs. The analysis of event logs can provide valuable insights for business process re‐engineering and enhancement. Process discovery, as one of the most challenging event log analysis techniques, aims to discover a business process model from an event log. Many process discovery approaches have been proposed in the past two decades, however, most of them suffer from efficiency problem when dealing with large‐scale event logs. Motivated by PageRank, we propose LogRank, a graph‐based ranking model, for event log sampling in this paper. The LogRank is capable of sampling a large‐scale event log to a smaller size that can be efficiently handled by existing discovery approaches. To support real‐life applications, we instantiate the LogRank model for two typical types of event logs, that is, simple event logs and lifecycle event logs. To quantify the quality of a sample log with respect to the original one, we introduce a general evaluation framework that can be instantiated for different quality metrics. The proposed sampling approach has been implemented in the open‐source process mining toolkit ProM. By experiments with both synthetic and real‐life event logs, we demonstrate that the proposed LogRank‐based sampling approach provides an effective means to improve process discovery efficiency as well as guaranteeing high quality of discovered models.

A Real-Time Audit Mechanism Based on the Compression Technique

Log management and log auditing have become increasingly crucial for enterprises in this era of information and technology explosion. The log analysis technique is useful for discovering possible problems in business processes and preventing illegal-intrusion attempts and data-tampering attacks. Because of the complexity of the dynamically changing environment, auditing a tremendous number of data is a challenging issue. We provide a real-time audit mechanism to improve the aforementioned problems in log auditing. This mechanism was developed based on the Lempel-Ziv-Welch (LZW) compression technique to facilitate effective compression and provide reliable auditing log entries. The mechanism can be used to predict unusual activities when compressing the log data according to pre-defined auditing rules. Auditors using real-time and continuous monitoring can perceive instantly the most likely anomalies or exceptions that could cause problems. We also designed a user interface that allows auditors to define the various compression and audit parameters, using real log cases in the experiment to verify the feasibility and effectiveness of this proposed audit mechanism. In summary, this mechanism changes the log access method and improves the efficiency of log analysis. This mechanism greatly simplifies auditing so that auditors must only trace the sources and causes of the problems related to the detected anomalies. This greatly reduces the processing time of analytical audit procedures and the manual checking time, and improves the log audit efficiency.

Online use and information seeking behaviour: institutional and subject comparisons of UK researchers

The paper reports on the results of the project ‘Evaluating the usage and impact of e-journals in the UK’. Using deep log analysis techniques, we evaluated the use of the Oxford Journals database in regard to life sciences, economics and history by 10 major UK research institutions. The aim of the study was to investigate researchers’ digital behaviour, and to ascertain whether it varied by subjects and disciplines, or in relation to the institutions. The findings revealed significant subject and institutional differences. Life scientists were the biggest users. Economists made the greatest use of abstracts. Historians proved to be the most active searchers. Research intensive universities were characterized by high volume use and short session times, light sessions, and sessions which utilized few of the search functions available. Open access journals featured strongly in the ranked lists of life sciences and history; and Google was an extremely popular means of accessing journal content, especially so in the case of historians.

The master builders: LAIRAH research on good practice in the construction of digital humanities projects

Although many digital humanities resources are being developed for online use, there is little understanding of why some become popular, whilst others are neglected. Through log analysis techniques, the LAIRAH project identified twenty-one popular and well-used digital humanities projects, and in order to ascertain the factors they had in common, which predisposed them to be well used, conducted in-depth interviews with the creators of these resources. This article presents the findings of the study, highlighting areas that developers should be aware of, and providing a set of recommendations for both funders and creators, which should ensure that a digital humanities resource will have the best possible chance of being used in the long term.

If You Build It Will They Come? The LAIRAH Study: Quantifying the Use of Online Resources in the Arts and Humanities through Statistical Analysis of User Log Data

There are now many online, digital resources in the humanities, and their creation is funded by various governmental, academic, and philanthropic sources. What happens to these resources after completion is very poorly understood. No systematic survey of digital resource usage in the humanities has ever been undertaken—and the factors for use and non-use of digital resources are unknown. The LAIRAH (Log Analysis of Internet Resources in the Arts and Humanities) Project is a 15-month long study into the factors which determine long-term use and neglect of digital resources in the Arts and Humanities. Using quantitative Deep Log Analysis techniques to understand real-time user behaviour and qualitative user workshops to gain an understanding of user approaches to digital resources in the arts and humanities, the study identifies factors that may predispose a digital resource to become used or neglected in the long-term. This article provides an overview of the techniques used in the LAIRAH project, and presents some preliminary results that may be of use to both the creators of digital resources in the humanities, and the funders of these projects, to ensure that significant intellectual effort and time, and financial resources, are not wasted in the creation of projects that are then neglected by the user community.

The information seeking behaviour of the users of digital scholarly journals

The article employs deep log analysis (DLA) techniques, a more sophisticated form of transaction log analysis, to demonstrate what usage data can disclose about information seeking behaviour of virtual scholars – academics, and researchers. DLA works with the raw server log data, not the processed, pre-defined and selective data provided by journal publishers. It can generate types of analysis that are not generally available via proprietary web logging software because the software filters out relevant data and makes unhelpful assumptions about the meaning of the data. DLA also enables usage data to be associated with search/navigational and/or user demographic data, hence the name ‘deep’. In this connection the usage of two digital journal libraries, those of EmeraldInsight, and Blackwell Synergy are investigated. The information seeking behaviour of nearly three million users is analyzed in respect to the extent to which they penetrate the site, the number of visits made, as well as the type of items and content they view. The users are broken down by occupation, place of work, type of subscriber (“Big Deal”, non-subscriber, etc.), geographical location, type of university (old and new), referrer link used, and number of items viewed in a session.

Mapping of the McMurray Formation for SAGD

Abstract The McMurray formation in north-eastern Alberta contains a substantial fraction of the world's currently identified oil resources. The areal extent of the deposit is in the order of 300 km north-south and 100 km east-west. It has been estimated(1) that over 40 billion barrels (6.35 × 109 m3) are potentially recoverable with steam assisted gravity drainage (SAGD) from dual well pairs. This is in addition to the substantial reserves already accessible from surface mining operations in locations with minimal overburden. The success of potential SAGD projects is greatly dependent upon the proper identification of regions with adequate pay, limited shale barriers, and controllable bottom water and/or top gas/water. The fluvial/estuarine nature of the formation greatly limits the predictability of trends when viewed in a local context. However, when regional behaviour is coupled with geostatistical techniques, an improved understanding of these critical parameters is possible. This paper applies a computerized log analysis technique to obtain the porosity, oil saturation and permeability from the McMurray formation utilizing digitized logs. Additional analysis is performed to obtain regions of effective net pay and other performance indices. This information is then presented through the use of regional mapping with a geostatistical approach. Introduction The Athabasca tar sand deposit is located in the northeast corner of Alberta, Canada. The major accumulation within this deposit is contained within the McMurray formation of early Cretaceous age. By various estimates, the OOIP of the McMurray formation exceeds 120 e09 m3 (750 billion bbls)(2). The commercial extraction projects currently operational in this region are based on surface mining techniques. However, only a small fraction of the resource is suitable for mining operations(2). The vast majority of the region is covered by overburden deposits that exceed any currently economic surface mining technique. These more deeply buried regions may prove to be amenable to the steam assisted gravity drainage (SAGD) process or some variant(3). Several pilot/demonstration plants have shown promising results with this technology, originally developed by the Alberta Oil Sands Technology Research Authority (AOSTRA) for the underground test facility (UTF)(4). The intention of this paper is to provide an overview of some of the computerized mapping techniques that are applicable to determining the extent of the possible SAGD resource in the McMurray formation. Reservoir Requirements for SAGD The SAGD process consists of a pair of parallel horizontal wells with a 3 8 m vertical separation. During normal operation, steam is injected into the upper/injection well, while oil and steam condensate are produced from the lower/production well. The energy transferred from the condensing steam heats the formation, greatly lowering the viscosity of the oil. This enables the oil to flow via gravity to the underlying production well. The process appears to be quite robust from a theoretical perspective. The two principal governing factors, conduction heat transfer into the cold reservoir and gravity flow of fluid to the production well, are only moderately influenced by many reservoir characteristics. A number of previous studies have explored the theoretical range of applicability of the SAGD process to generic heavy oil reservoirs(5, 6).

The Role of Hydrogeology in Petroleum Development Oman

ABSTRACT A hydrogeological study of the South Oman Salt basin in particular, and the Oman basin in general, was carried out by Petroleum Development Oman to support basin modelling. The study focused on major aquifer systems: the Tertiary Umm Er Radhuma aquifer and the Paleozoic Haima-Haushi aquifers. Temperature, water quality, radiocarbon age dating and potentiometric data was used to determine the flow, thermal and salinity regimes of the systems. The resulting models were then used to define areas where oil biodegradation due to meteoric water influx can be expected. A review of oil chemistry data confirmed that fields in these areas have the highest API gravity oil demonstrating that hydrogeological modelling can be beneficial in predicting oil quality for prospect evaluation. An additional benefit of this study was the development of a formation water resistivity model for enhancing log analysis techniques.

Reservoir Characterization Utilizing a Low Cost Resistivity-Porosity Crossplot and an Interactive Spreadsheet: ABSTRACT

In mature oil and gas producing provinces it is necessary to find a low cost, but reliable method to evaluate oil/gas field reservoirs for improving recovery. If a field has very limited core data, and production is commingled, the reservoir study will often need to rely on wireline logs of many types and from different logging companies. The challenge is to fully evaluate each well with a technique in which reservoir characterization is comparable and consistent so as not to [open quotes]build[close quotes] anomalies that are not really present. A reliable technique is the [open quotes]Super[close quotes] Pickett plot that is an extension of the classical log-log porosity/resistivity plot. The [open quotes]Super[close quotes] Pickett plot is a pattern recognition tool where points are linked successively by depth and annotated with lines corresponding to bulk volume water and permeability in addition to the standard water saturation, porosity, and resistivity lines. Subtle trends and patterns of these depth-linked series of points are used to characterize each reservoir. Color is used to display additional attributes such as gamma ray values, photo electric values, capillary pressure and lithology (RHOMAA/UMAA). The computational power and flexibility of a spreadsheet computer program is utilized with a colormore » graphic display to expedite the characterization of complex reservoirs. Key reservoir parameters derived from this analysis are mapped using the software to portray interwell heterogeneities and evaluate continuity. This log analysis technique can be tailored to varied, complex heterogeneous reservoirs both siliciclastics and carbonate lithology. Results provide the means to promote communication among geologists and engineers. The use of this technique is illustrated by a study of the Schaben Field in Ness County, Kansas, which is being supported by a Class II DOE grant.« less

Formation Evaluation of Exploration Coalbed-Methane Wells

SummaryDevelopment of coalbed-methane reservoirs requires accurate determination of gas in place and natural fracture permeability. This paper outlines steps needed to evaluate reservoir properties and to predict well deliverability. Procedures are documented with data and analyses from San Juan basin development projects. The paper includes an example of the accuracy of gas-content determinations from pressure-, sidewall-, and conventional-core and drill-cuttings samples; presents specifications for complete log suites and analysis techniques for gas-content estimates; discusses drillstem-test (DST) procedures; and outlines prediction of well deliverability.

Reservoir Characteristics And Petroleum Potential of the Bakken Formation, Southwestern Manitoba

Abstract The Bakken Formation lies between the Upper Devonian Three Forks (Lyleton) Formation and the Mississippian Lodgepole Formation. The productive Middle Member of the Bakken Formation is a very fine- to fine-grained, argillaceous, dolomitic sandstone to siltstone. This paper outlines the geological setting in southwestern Manitoba and presents the general stratigraphy and lithology of the Bakken Formation. Conventional log analysis techniques result in erroneous calculations of porosity and water saturation for the litho-logically complex Middle Member. This paper illustrates the log analysis techniques developed to determine accurate porosity and water saturation for the Bakken Middle Member. Reservoir properties and production performance under both primary and secondary recovery for Bakken pools in Manitoba is also presented. Low drilling and completion costs; the province's drilling incentive program; high quality oil (over 40 ° API); and, original oil-in-place per pool of 500 × 103m3, make the Bakken an attractive exploration target in Manitoba. Bakken exploration and development prospects are discussed, including a brief overview of Bakken source rock and migration pathways and sample development drilling economics. Introduction The Bakken discovery well in Manitoba, 13-21-10-29(WPM) was drilled in the Daly Field in 1985. Production from the Bakken Formation in Manitoba is presently restricted to the Daly field (Figure 1), where it produces from the Middle Member. At year end 1992, there were 38 active Bakken producers in five pools in the Daly Field. Total oil production from the Bakken in Mamtoba in 1992 was 23.6 x. 103m3. As of December 31, 1992 cumulative Bakken oil production was 127.9 × 103m3 and remaining established Bakken reserves totalled 97.4 × 103m3. FIGURE 1: Manitoba oil fields and producing areas. (Available in full paper) Geological Setting The Williston Basin occupies portions of North Dakota, South Dakota, Montana, Saskatchewan and Manitoba (Figure 2). The productive areas of southwestern Manitoba are located on the northeastern flank of the Williston Basin. Paleozoic, Mesozoic and Cenozoic rocks form a basin ward-thickening wedge of sedimentary strata that reaches a thickness of 2,300 m in the extreme southwest corner of the province. The Mississippian stratigraphy in the Manitoba portion of the Williston Basin is shown in Table l. In southwestern Manitoba, Mississippian strata, divided into four formations, from the uppermost part of the Paleozoic sequence. These formations, in ascending order are: Bakken Formation, Lodgepole, Mission Canyon and Charles formations of the Madison Group. Approximately 70% of Manitoba's current oil production is from the Bakken, Lodgepole and Mission Canyon formations. The geological discussion in this paper focuses exclusively on the stratigraphy and lithology of the Bakken Formation. Stratigraphy The Bakken Formation is a thin, clastic unit that straddles the Devonian-Mississippian boundary. In southwestern Manitoba, the Bakken Formation uncomfortably overlies the Upper Devonian Three Forks (Lyleton) Formation and underlies the Mississippian Lodgepole Formation. The Bakken Formation is divided into three informal units: the Lower Member, Middle Member and Upper Member (Table 1). The three members exhibit an on-lapping relationship, each successively younger member extensively overlapping the older member(1). The Upper and Middle Members are present throughout southwestern Manitoba.

Some Considerations in the Application of Wireline Logging to Environmental Investigations: ABSTRACT

ABSTRACT Wireline log interpretation and analysis techniques were developed by the petroleum industry to identify and quantify pore fluids in petroleum reservoirs, to characterize the properties of reservoirs, to identify lithology, and to facilitate geologic interpretation. In subsurface environmental investigations the information required is almost identical. Such investigations must identify and quantify pore fluids in contaminated media, quantify contaminant concentrations in ground water, determine the hydrologic properties of aquifers, and characterize subsurface geology. Given the great similarity of information required, classical petroleum log interpretation and analysis principles should have direct and useful application to environmental questions. However, attempts to apply these principles have often produced disappointing results that can many times be attributed to differences in the physical environments, primarily hole and formation conditions, in which logging data are acquired by the two disciplines. Classical wireline logging theory is based on central assumptions regarding the physical environment in which tools are operated. Environmental logging may occur both above and below the water table, in formations that contain air and/or fresh water, in holes that may be filled with air, water, or mud, and where the salinity of the formation and borehole fluids may be similar. In these cases those central assumptions are commonly not valid. Geologists and log analysts must be aware of these differences and how they affect the various types of wireline logs. Some of these problems may be resolved by further research, rethinking of existing theory, and the development of new analytical techniques.

A Statistical Analysis Of The Early Peace River Thermal Project Performance

Abstract A statistical correlation of well performance indices with geology is described for the steam soak phase of the Peace River Expansion Project (PREP), a thermal recovery project operated by Shell Canada Ltd. As expected from a confirmed, permeability-dominated steam underride through a basal lean bitumen zone, well performance correlates only with geology of the lean zone, and not at all with that of the overlying rich bitumen zone during this stage. However, the existence of extensive cross-correlations among the geological parameters, which in at least one instance has introduced a spurious correlation for calendar day oil rate, suggests that log analysis techniques need to be optimized in studies of this kind. In additionto the conventionally calculated bulk fraction shale volume Vsh)from logs, the introduction of other shale parameters is advantageous, and is discussed. Introduction In recent years there has been a growing interest in statistical studies of oil well performance indicators. Apart from the most common analyses, dictated by the need to reduce large data sets to some manageable size, statistical correlations of productiondata against geological parameters might be used to test theories of reservoir depletion that are provided by modelling. This work is intended to test a depletion theory, with the use of public data from the thermal Peace River Expansion Project (PREP), operated by Shell Canada Ltd. in northwestern Alberta. The PREP Project Shell Canada Ltd. has been operating the Peace River Expansion Project since 1986, following a very successful pilot which, as of October 1989, has recovered 53% of oil-in-place by a pressure cycle steam drive process(1). The project recovers bitumen from the 30 m thick Bluesky/Gething sands which have a thin basal zone of relatively low oil saturation (50% of pore volume) and high permeability. This lean pay zone, varying in thickness from 0.3 m to 7 m, allows injection of stream at high rates below fracture pressure in all but eight wells. All wells are perforated in this zone, except in Pads 12 and 15, where shale in the basal zone forced perforation in the rich zone, Thus, a permeability-dominated steam underride(2) permits heating or the richer zone from below(3). The operating strategy and facilities at the Peace River project have been described on several occasions(4–8); in essence it consists of alternating periods of high steam injection and low production rates (pressure-up) with periods of low or no steam injecrion and high production rates (blowdown). Figure 1 shows the well patterns at the Shell Peace River Project. To the west lies the original well cluster of the pilot (PRISP) project, consisting of seven inverted seven spots, Four clusters of thirteen patterns each represent the expansion project which will be considered in this paper. The small irregular "Multisoaks" pattern remains from a previous cyclic steam pattern. While the three eastern wells of this pattern have been incorporated into cluster A, they will be excluded owing to their prior operations history.