Hydraulic Fracturing Treatment Optimization Using Machine Learning

Friction reducers are chemicals used in hydraulic fracturing to reduce friction between fracturing fluid and the wellbore walls, helping to overcome tubular drag at high flow rates. High viscosity friction reducers are increasingly used due to operational and economic benefits, but their optimal concentration for each stage of fracturing is not well studied. As a result, oil and gas companies often use more friction reducers than necessary to ensure designed injection rates are achieved and to avoid screening out, resulting in excess use of FR and economic losses. The primary goal of this Thesis is to fully comprehend and quantify the performance of the Hydraulic Fracturing Friction Reducer in a wide range of concentrations used in the completion of eight horizontal Marcellus Shale wells, as well as to optimize the amount of friction reducer required to complete the job and reduce the cost associated with it. Several machine learning approaches have been employed and it is going to be discussed with its approach, and data from BOGGESS wells have been evaluated to optimize the amount of friction reducer. Finally, an economic analysis will determine the project\u27s net present value (NPV) and accumulated savings of FR cost per stage. Data collected from 1500 wells stimulated in Marcellus Shale. The data were reviewed to define the problem statement of using too much FR concentration. However, due to confidentiality agreement, data from Boggess pads from the MSEEL project including Boggess 1H, 3H, 5H, 9H, and 17H will be used. The collected data was the one second reading for the completion process for each stage from MSEEL project website

Misc. Pub. 92-1

I submit herewith the annual report of the Agricultural and Forestry Experiment Station, School of Agriculture and Land Resources Management, University of Alaska Fairbanks, for the period ending December 31, 1991. This is done in accordance with an act of the Congress, approved M arch 2, 1887, entitled "An act to establish Agricultural Experiment Stations, in connection with the Agricultural Colleges established in the several states under the provisions of an act approved July 2,1862, and under the acts supplementary thereto," and also of the act of the Alaska Territorial Legislature, approved March 12,1935, accepting the provisions of the act of Congress. James V. Drew, DirectorStatement of Purpose -- Drastic Retreat of Columbia Glacier Shows Natural Reforestation at Work -- Reindeer Range and Productivity Studied in Beringia Heritage International Park -- Radio Collars Aid Research into Reindeer Calf Mortality -- Moose Browse on Site of Mining Reclamation Study -- Developing Marketing Strategies for Alaska's Agricultural Entrepreneurs -- R.S. 2477— The Key to Access Across Federal Lands -- Plant, Animal and Soil Sciences -- Forest Sciences -- Resources Management -- Grants and Funds -- Financial Statement -- Publications -- Staf

Speleothem growth intervals reflect New Zealand montane vegetation response to temperature change over the last glacial cycle

Flowstone speleothem growth beneath Mount Arthur, New Zealand shows a clear relationship to vegetation density and soil development on the surface above. Flowstone does not currently form beneath sub-alpine Nothofagus forest above ca. 1000–1100 m altitude but U-Th dating shows it has formed there during past intervals of warmer-than-present conditions including an early–mid Holocene optimum and the last interglacial from ca. 131–119 ka. Some flowstones growing beneath ca. 600 m surface altitude, currently mantled with dense broadleaf-podocarp forest, grew during full glacial conditions, indicating that local tree line was never below this altitude. This implies that Last Glacial Maximum annual temperature was no more than ca. 4 °C cooler than today. Flowstone growth appears to be a robust indicator of dense surface vegetation and well-developed soil cover in this setting, and indicates that past interglacial climates of MIS 7e, 5e, the early–mid Holocene and possibly MIS 5a were more conducive to growth of trees than was the late Holocene, reflecting regional temperature changes similar in timing to Antarctic temperature changes. Here, flowstone speleothem growth is a sensitive indicator of vegetation density at high altitude, but may respond to other factors at lower altitudes

Third Earth Resources Technology Satellite Symposium. Volume 3: Discipline summary reports

Presentations at the conference covered the following disciplines: (1) agriculture, forestry, and range resources; (2) land use and mapping; (3) mineral resources, geological structure, and landform surveys; (4) water resources; (5) marine resources; (6) environment surveys; and (7) interpretation techniques

Exploration for fossil and nuclear fuels from orbital altitudes

Results from the ERTS program pertinent to exploration for oil, gas, and uranium are discussed. A review of achievements in relevant geological studies from ERTS, and a survey of accomplishments oriented towards exploration for energy sources are presented along with an evaluation of the prospects and limitations of the space platform approach to fuel exploration, and an examination of continuing programs designed to prove out the use of ERTS and other space system in exploring for fuel resources

The Application of Data Analytics and Machine Learning for Formation Classification and Bit Dull Grading Prediction

Master's thesis in Petroleum Engineering.The oil and gas industry, especially its upstream part generates a massive amount of data. The proper data collection and processing are the vital elements of reducing the non-productive time and increasing the drilling operations efficiency. The major part of each well program is the drill bits selection. It is the most important tool which does slicing or crushing downhole and highly affects the overall drilling performance. However, drill bit selection is mostly accomplished through lessons learned from previous runs as well as bit grading after each run. These methods are highly subjective and usually based on the engineer’s experience. The abundance of field data with data analytics and machine learning capabilities are a perfect combination for creating reliable data-driven models. The main objective of this study is to create robust models that are able to classify the formation based on drilling parameters as well as estimate the bit dull grading based on drilling parameters and the formation. In order to achieve the aforementioned goals, the disclosed Volve filed dataset was meticulously processed and analyzed. The models were created for each of the well sections by using the Python, especially the pandas and scikit-learn libraries. However, after running the first simulation, models usually showed unsatisfactory accuracy. In order to increase models performance, the code was written to find the best parameter for each machine learning technique. Even though the bit dull grading model has a valid algorithm, the input parameters are hard to find, due to the lack of literature and patterns. Obtained results proved that the machine learning technique may be successfully implemented to solve the everyday problems in the oil and gas industry. Moreover, the outcome should help in the well planning process, enables to decrease the number of trips and improves overall drilling phase efficiency. The process could eliminate the trial and error drill bits selection and ensure more efficient and effective decision-making process

Nimbus 6 Random Access Measurement System applications experiments

The advantages of a technique in which data collection platforms randomly transmit signal to a polar orbiting satellite, thus eliminating satellite interrogation are demonstrated in investigations of the atmosphere; oceanographic parameters; Arctic regions and ice conditions; navigation and position location; and data buoy development