ABSTRACT
The COVID-19 pandemic forced Canadian oil and gas operators to cut crude oil production by almost 1 MMb/d in the first half of 2020 due to low oil prices driven by reduced demand. This study explores the forecast and EUR performance of unconventional horizontal oil wells producing from the Duvernay Formation in central Alberta that were shut-in versus those that continued to produce uninterrupted throughout the reduced production period. How were forecasted production and EURs impacted? Did the manner in which the wells were completed play a role? This paper investigates these questions and more in a regional case study of 95 unconventional Duvernay oil wells using public data and a fully automated, physio-statistical, predictive analytical production forecasting tool. The bases of the performance comparison were the results of a 10-year forecast and EUR outlook for the wells evaluated in January, 2020 before the production slow down, and then re-evaluated in January, 2021, 12 months later, after the wells that were shut-in were back on production. In general, wells that continued producing uninterrupted throughout the study period exhibited significantly improved forecast and EUR performance over wells that were shut-in. Analyzing the performance of the largest field (Cygnet with 32 wells), with respect to lateral length, the results pointed to shorter wells that were shut-in exhibiting the poorest performance, where the wells' EUR performance degraded by 7% on average. The proppant intensity study for the same wells told a similar story, with shut-in wells with smaller fracs exhibiting negligible EUR improvement (0.4%) compared to the other categories of wells, with respect to frac size and shut-in status. A proximity study investigated two pads, one with only shut-in wells and the other with only non-shut-in wells, with the results pointing to competitive drainage between individual wells despite the overall performance of a given pad being neutral. Copyright 2022, Unconventional Resources Technology Conference (URTeC)
ABSTRACT
The relationship between the economies of various countries and their dependence on the world markets indicate that for econometric analysis of the impact of external shocks on a particular economy, it is necessary to use a model of the global economy. The aim of this paper is to build a global vector autoregression model (GVAR), including Russia as one of the regions, and to obtain the impact of some external economic shocks on Russian macroeconomic indicators. We build a model that includes 41 of the world's major economies, including Russia, and the oil market. The special features of our model are structural shifts in the dynamics of Russian output and the new specification of oil supply and oil demand. Impulse response functions are used to obtain quantitative estimates. In this paper, we analyze the reaction of outputs, oil production volumes and oil prices in response to the output shocks of China and the United States. In response to the negative shock of output in the world's leading economies, outputs in the rest of the world declined for at least the first year after the shock. There was also a significant decline in oil prices and no significant change in oil production volumes in most countries. In addition, as part of the conditional forecast, we estimated the impact of the decline in global demand due to the Covid-19 pandemic on the Russian GDP as 1,3% drop. The rest of the decline in Russian GDP can be attributed to the internal effects of the pandemic (lockdown). We also obtained a scenario forecast of the dynamics of Russian GDP depending on a decrease in trade and Russian oil price discount, within which the fall in Russian output could reach 3.3% in 2022. © 2023 Publishing House of the Higher School of Economics. All rights reserved.
ABSTRACT
The past few years have been challenging for the oil and gas industry. Many processes and operations have needed to adapt to lower oil and gas prices, caused in part by the COVID-19 pandemic. Understanding reservoir producibility and proving reserves are keys to generating a reservoir field development plan (FDP). However, the different processes to obtain such answers are strongly dependent on cost. The value of information is an extremely important criterion for operators to decide whether to proceed with their discoveries. In an interval pressure transient test (IPTT), a formation tester is used to pump a fluid from a single point or small interval of the formation into the wellbore. Zones of interest can be isolated and tested separately zone by zone. Mud filtrate and reservoir fluids are pumped continuously using the downhole pump, and a downhole fluid analyzer (DFA) is used to monitor the fluid cleanup process. The post-pumping p pressure buildup can be analyzed in a similar manner to traditional well test analysis. Such IPTT have been available since 1980s;however, comparisons of IPTT to actual well tests and other permeability measurements were rarely published until the early 2000s. IPTT have been widely used in the past 20 years, especially in combination with dual packers, and more recently with single packers. Operation efficiency and safety have improved significantly. However, interpretation of the pressure transient obtained from an IPTT is not always well understood. Frequently asked questions (FAQs) include the following: 1. What is an IPTT or a vertical interference test (VIT)? 2. How does an IPTT compare with other permeability measurements? 3. What are the different scales of pressure transient data? 4. How do we upscale zone permeability to an entire reservoir interval? 5. What is next? This paper will address these questions using both reservoir simulation and field data. The field examples are from different environments, ranging from shallow marine to turbidite to deepwater environments, with different fluid systems, such as black oil, heavy oil, waxy oil, gas, and gas condensate. Geographically, the field data include examples from South East Asia and the Middle East. Permeability obtained from pretests, IPTT, nuclear magnetic resonance (NMR), core analyses, and well testing will be compared. Recently deep transient testing (DTT) has been introduced in the industry. With DTT, we can flow faster and longer than previously possible with formation testers, enabling pressure transient analysis in higher permeability and thicker formation. Further data quality improvements come from new, high-resolution gauges deployed with an intelligent wireline formation testing platform. This paper includes a review of the DTT method with several field examples. Finally, the advantages and disadvantages of the different testing methods are discussed relative to the test objectives, with the intent to provide a cost-effective data selection method to ensure sufficient FDP input and to justify the value of investment to the relevant stakeholder. Copyright © 2023, International Petroleum Technology Conference.
ABSTRACT
The relationship between the economies of various countries and their dependence on the world markets indicate that for econometric analysis of the impact of external shocks on a particular economy, it is necessary to use a model of the global economy. The aim of this paper is to build a global vector autoregression model (GVAR), including Russia as one of the regions, and to obtain the impact of some external economic shocks on Russian macroeconomic indicators. We build a model that includes 41 of the world's major economies, including Russia, and the oil market. The special features of our model are structural shifts in the dynamics of Russian output and the new specification of oil supply and oil demand. Impulse response functions are used to obtain quantitative estimates. In this paper, we analyze the reaction of outputs, oil production volumes and oil prices in response to the output shocks of China and the United States. In response to the negative shock of output in the world's leading economies, outputs in the rest of the world declined for at least the first year after the shock. There was also a significant decline in oil prices and no significant change in oil production volumes in most countries. In addition, as part of the conditional forecast, we estimated the impact of the decline in global demand due to the Covid-19 pandemic on the Russian GDP as 1,3% drop. The rest of the decline in Russian GDP can be attributed to the internal effects of the pandemic (lockdown). We also obtained a scenario forecast of the dynamics of Russian GDP depending on a decrease in trade and Russian oil price discount, within which the fall in Russian output could reach 3.3% in 2022. © 2023 Publishing House of the Higher School of Economics. All rights reserved.
ABSTRACT
This study examines the predictive power of oil shocks for the green bond markets. In line with this aim, we investigated the extent to which oil shocks could be used to accurately make in- and out-of-sample forecasts for green bond returns. Three striking findings emanated from our results: First, the three types of oil shock are reliable predictors for green bond indices. Second, the performances of the predictive models were consistent across the different forecasting horizons (i.e. H = 1 to H = 24). Third, our findings were sensitive to classifying the dataset into pre-COVID and COVID eras. For instance, the results confirmed that the predictive power of oil shocks declined during the crisis period. We also discuss some policy implications of this study's findings. © 2022 The Author(s)
ABSTRACT
This study investigates the impacts of crude oil-market-specific fundamental factors and financial indicators on the realized volatility of West Texas Intermediate (WTI) crude oil price. A time-varying parameter vector autoregression model with stochastic volatility (TVP-VAR-SV) is applied to weekly data series spanning January 2008 to October 2021. It is found that the WTI oil price volatility responds positively to a shock in oil production, oil inventories, the US dollar index, and VIX but negatively to a shock in the US economic activity. The response to the EPU index was initially positive and then turned slightly negative before fading away. The VIX index has the most significant effect. Furthermore, the time-varying nature of the response of the WTI realized oil price volatility is evident. Extreme effects materialize during economic recessions and crises, especially during the COVID-19 pandemic. The findings can improve our understanding of the time-varying nature and determinants of WTI oil price volatility. © 2022
ABSTRACT
Since oil plays an important role in the economy of Azerbaijan, the events in the global oil market deeply affect the national economy. Moreover, the COVID-19 pandemic influenced the economy of Azerbaijan, in which oil and gas have a significant place. In April 2020, the price of one barrel of oil on the world market fell to $1. One reason for this was the decrease in oil demand due to the lockdown regime implemented by many countries due to the rapid outbreak of the COVID-19 pandemic, and another reason was that the OPEC (Organization of the Petroleum Exporting Countries) countries could not agree on reducing oil production. The aim of this research is to show the impacts of oil prices on gross domestic product (GDP) of Azerbaijan, the growth rate of GDP, and the amount of oil production in Azerbaijan in 2009-2018. The hypothesis of the research is that oil prices seriously influence the economy of Azerbaijan and there is a correlation between the growth rate of Azerbaijan's gross domestic product and the oil prices. The study starts with a brief description of the history of Azerbaijan's oil industry, followed by oil industry's importance in the economy of Azerbaijan, the role in foreign economic relations, and the effects on the economy of country. The quantitative method was used as a key research method. The data used in the analysis of this study were collected according to the literature scanning method, which is one of the data collection techniques. Further, descriptive statistics technique, which is a quantitative data analysis technique, was used to analyse the data. The findings show that the changes in oil prices in 2009-2018 directly affect the Azerbaijan's gross domestic product, the growth rate of GDP, and the amount of oil production in Azerbaijan. Thus, as oil prices increase, the growth rate of the country's gross domestic product and GDP increase and decrease as oil prices decrease. © Sarkhanov T. Text. 2022.
ABSTRACT
As societies rely increasingly on computers for critical functions, the importance of cybersecurity becomes ever more paramount. Even in recent months there have been attacks that halted oil production, disrupted online learning at the height of COVID, and put medical records at risk at prominent hospitals. This constant threat of privacy leaks and infrastructure disruption has led to an increase in the adoption of artificial intelligence (AI) techniques, mainly machine learning (ML), in state-of-the-art cybersecurity approaches. Oftentimes, these techniques are borrowed from other disciplines without context and devoid of the depth of understanding as to why such techniques are best suited to solve the problem at hand. This is largely due to the fact that in many ways cybersecurity curricula have failed to keep up with advances in cybersecurity research and integrating AI and ML into cybersecurity curricula is extremely difficult. To address this gap, we propose a new methodology to integrate AI and ML techniques into cybersecurity education curricula. Our methodology consists of four components: i) Analysis of Literature which aims to understand the prevalence of AI and ML in cybersecurity research, ii) Analysis of Cybersecurity Curriculum that intends to determine the materials already present in the curriculum and the possible intersection points in the curricula for the new AI material, iii) Design of Adaptable Modules that aims to design highly adaptable modules that can be directly used by cybersecurity educators where new AI material can naturally supplement/substitute for concepts or material already present in the cybersecurity curriculum, and iv) Curriculum Level Evaluation that aims to evaluate the effectiveness of the proposed methodology from both student and instructor perspectives. In this paper, we focus on the first component of our methodology - Analysis of Literature and systematically analyze over 5000 papers that were published in the top cybersecurity conferences during the last five years. Our results clearly indicate that more than 78% of the cybersecurity papers mention AI terminology. To determine the prevalence of the use of AI, we randomly selected 300 papers and performed a thorough analysis. Our results show that more than 19% of the papers implement ML techniques. These findings suggest that AI and ML techniques should be considered for future integration into cybersecurity curriculum to better align with advancements in the field. © American Society for Engineering Education, 2022