Diagnostic fracture injection tests (DFIT's), or "mini-fracs" are often used to gauge many reservoir and fracture design parameters. However, DFITs are not always conducted in conjunction with the main completions work. This paper proposes a novel workflow to determine the instantaneous shut-in pressure (ISIP) from readily available completions data. This is a valuable parameter in itself as related to the least principal in-situ stress states as demonstrated by the stress change relationships near faults in
Directly using completions data from fracture stimulation operations, the authors have leveraged on the water-hammer signature in bottom-hole pressure data during completions to process the ISIP for each completions stage. Within this study, completions data from ~2100 stages from ~300 horizontal Montney formation wells were analyzed. A MATLAB script was used to automate the derived ISIP stress trends over the Montney formation and to deduce the ISIP in a consistent format.
This novel workflow also validates the expected in-situ stress trends at depth, with a relationship of high ISIP gradients closer to fault zones similar to stress change behaviour as shown in
Considering the continued push for higher fluid and sand loading in industry in the development of unconventional assets as an economic driver, there also exists a large and tangible corporate citizenship opportunity of mining real time completions dark data with the possibility of relating that live feed as a prescriptive tool to mitigate reactivation of critically stressed faults. This case study focuses on the Montney formation as a basis for processing easily available data from standard operations in an effort of systematically designating areas prone to seismicity risk in future hydraulic fracturing operations based on automated real-time analytics of dark data.
Within a single field geophysical survey results always have a significant amount of data with a considerable variability and heterogeneity. This allows to classify geophysical data as a Big Data. Data scientists and software developers are increasingly recommending the use of machine learning techniques for data processing and interpretation. ML algorithms allow one to extract the most complete amount of useful information, reduce time costs, minimize the subjective factor in the decision-making process, etc. Early testing of these approaches began in the 60s, active practical implementation consisted in the 90s due to the large-scale implementation of seismic studies in 3D CDP modification 1. The emergence of new algorithms, modifications of the original data, the development of computational resources support the relevance of this topic at the present time. In seismic data interpretation machine learning approaches provide high performance in the process of automatic horizons picking, fault tracing, seismic facies analysis, sesimic inversion, reservoir prediction, etc. At the stage of seismic facies analysis application of the ML algorythms is especially effective since in the process of multiattribute classification the initial dataset increases severalfold in accordance with the number of calculated attributes 5-7, 9, 10.
Current multistage hydraulic fracturing operations in shale are costly, environmentally challenging and inefficient. Multistage hydraulic fracturing operations already represent close to 60% of the total drilling and completion cost for each shale well. The industry studies reported that based on data evaluated in multiple shale basins in North America alone that up to 50% of the clusters and stages do not produce in geometric completion design. Shale E&P operators need more accurate, cost-efficient, timely and actionable data on the performance of individual fracturing stages and intra-well communication to enable improved decision-making and optimization of multistage hydraulic fracturing and completion strategy, as well as overall field development.
This paper will describe a revolutionary smart tracer portfolio testing and design for multistage hydraulic fracturing stimulation. The technology enables the next generation of smart tracers coupled with advanced sub-atomic measurements that significantly reduce the completion cost and double the efficiency of the hydraulic fracturing treatments. An automated process with stringent quality control assured precise tracer addition onsite and provided accurate and actionable completion diagnostics results at fraction of the cost for high-cost measurements (e.g., PLT, DTS & DAS).
The integration of smart tracer portfolio with intelligent-completion diagnostics for E&P customer enabled by performance-flow-profile data. This data used to optimize completion strategies, achieve optimal production per foot, and reduce completion cost. Follow-up big-data analytics and 3D fracture-modeling delivered accurate, calibrated, actionable, and cost-effective completion-diagnostics results. Since tracer data are captured over several months, E&P operators are captured access to continuous flow profiling data to optimize well performance routinely when new completion-diagnostics results are received. This will enable E&P operators to significantly reduce operating cost and optimize production in shale wells.
The fourth industrial revolution, or Industry 4.0, has the potential to disrupt every industry, including the oil and gas industry through large-scale automation, robotics, artificial intelligence, and big data analytics. Young professionals (YPs) will be the main engine responsible for the development of many Industry 4.0 technologies in the oil and gas industry. This survey by the SPE Saudi Arabia section intends to gauge the pulse of YPs and their readiness to engage in it. The results of the survey will be discussed with executives from major companies during the executive panel titled "Energy Meets Intelligence" at the SPE-KSA Annual Technical Symposium and Exhibition to be held in April in Dammam, Saudi Arabia.
The Simplified Series, one of the most successful programs of the SPE Aberdeen Section young professionals (YPs) kicked off in September after a summer break with a presentation on Big Data and how to make sense of information and analytics in the oil and gas industry. Instead of focusing on revenue enhancement, Steven Rossiter, managing director at AgileTek focused his presentation on giving useful insights on how big data can be used in the industry to improve the safety, quality control, and the ability to forecast and move to evidence-supported decision-making instead of relying on the intuition of individuals. The event was organized and hosted by the SPE Aberdeen Section YP Committee and enjoyed a large turnover of 77 professionals. It took place during the SPE ENGenious Symposium–SPE's new global symposium and exhibition aimed at driving radical digital and technological change across the upstream oil and gas industry. The YPs had the opportunity to meet the 2018 SPE President Darcy Spady and SPE South, Central East Europe Director Jean-Marc Dumas.
Workplace safety is a main objective of any company working in the oil and gas business. The processes have been developed and established over the past decades based on individual experiences and causal pathways. The exhaustion of technical and administrative barriers has led to the introduction of behavioral safety. Recent advances in data technology and machine learning have disrupted many businesses and processes and can lead to a new paradigm in workplace safety as well.
In this case study we demonstrate the application of data science and predictive analytics to aid the HSE function and prevent accidents. We have analyzed operational and accident data from the past 10 years at a leading oil and gas company to quantify the effectiveness of their safety programs.
We have determined how many accidents each program actually prevents, and is able to prevent in an optimal setting. We have determined the optimal level of engagement for each program, and at what level diminishing returns set in.
We have further developed a predictive model to forecast the occurrence of accidents one month ahead of time. In this way the HSE function is able to focus on 15% of locations to control 69% of the accidents. The forecast was also able to predict accidents at locations where one would traditionally not expect accidents to happen, such as locations with low activity.
This paper shows the potential for improvement that is possible with the emerging big data, artificial intelligence and machine learning tools specifically in the field of workplace safety.
With the busy and intense streets of Seoul and the calm and refreshing nature of its mountainous range, South Korea makes a unique destination that everyone should experience at least once. Logos can tell a story by illustrating a business’ brand, vision, ambitions, and core values. In this edition of Young Professionals’ Guide To, we explore two major oil and gas company logos. With a theme on Big Data, this year’s ATCE has plenty in store for young professionals. Data story consumers are focused on summarized results and highlights instead of details of the analysis.
Drilling activity in US shale plays is slowing as operators encounter higher prices for labor, equipment, and services, and lower prices for the oil and gas produced. Optimization of maintenance costs is among operators’ main concerns in the search for operational efficiency, safety, and asset availability. The ability to predict critical failures emerges as a key factor, especially when reducing logistics costs is mandatory.
Digital technologies serve as a primary theme of this year’s group, with a few environmentally conscious firms included in the mix. The large independent put together a team of data scientists, software developers, and petrotechnical staff to create a forward-looking vision for how to use digital technology to solve problems. Do women in academia face the same challenges as their peers in industry? Using maglev technology, a new artificial lift system seeks to boost production output by sucking down reservoir pressure from inside the wellbore and from inside the reservoir. The projects are designed to reduce technical risks in enhanced oil recovery and expand application of EOR methods in conventional and unconventional reservoirs.
Keeping Up With the Digital Age: What is Data Analytics all about? This interview explores the opportunities, challenges, and what young professionals need to know to have a rewarding career in drilling data analytics. Data story consumers are focused on summarized results and highlights instead of details of the analysis. It’s a data scientist’s responsibility to identify the significance of the data and to present it in a simple but scientific manner. Statoil's Acting head of Digital Centre of Excellence shares the company's digital road map.