Survey Analysis of Enhanced Oil Recovery (EOR) Laboratory towards Featured Research Laboratory

The EOR Research Laboratory is a laboratory that was independently pioneered by the Department of Petroleum Engineering UPN "Veteran" Yogyakarta. The EOR Research Laboratory needs to be improved especially for the existing spatial layout to support the ongoing and future research. This Institutional Research will cover the planning process of spatial layout design, spatial layout realization, internal and external EOR Research Laboratory socialization, and at the end of the study, effective and efficient governance will be applied to adapt to the current pandemic conditions. The method used in this research is quantitative in the form of socialization, questionnaires, and survey analysis to find out how EOR Laboratory is well known among students. This research is expected to be able to introduce the EOR Research Laboratory in a wider range and carrying out sustainable research in the future so that it will support the planning of the laboratory to be the Leading EOR Research Laboratory at the Department of Petroleum Engineering, UPN "Veteran" Yogyakarta.

Increasing the Publication of Scientific Articles to Support Key Performance Indicators by Providing Publication Space Facilities in the Petroleum Engineering Department

This study was used to identify and increase the interest of students of the Department of Petroleum Engineering, Faculty of Mineral Technology, UPN "Veteran" Yogyakarta in publishing scientific articles. the publication of scientific articles has an effect on improving the quality of majors in a university so that it is necessary to support the increase in the publication of scientific articles. The method used in this research is quantitative in the form of socialization, questionnaires, and holding competitions to explore potential interests and talents, as well as awarding student work appreciation. The results obtained from this study are in the form of increasing the quality of publications from competitions conducted with 3 scientific articles with national journal standards and 4 standard posters. From this progress, it is hoped that interest in writing standardized scientific articles will continue to increase, thus the quality of education and achievement of scientific work indicators of a university is achieved.

Human Factors in Domain Adaptation Within the Oil and Gas Industry

A serious issue facing many oil and gas companies is the uneasiness among the traditional engineering talents to learn and adapt to the changes brought about by digital transformation. The transformation has been expected as the human being is limited in analyzing problems that are multidimensional and there are difficulties in doing analysis on a large scale. But many companies face human factor issues in preparing the traditional staff to realize the potential of adaptation of AI (Artificial Intelligence) based decision making. As decision-making in oil and gas industry is growing in complexity, acceptance of digital based solutions remains low. One reason can be the lack of adequate interpretability. The data scientist and the end-users should be able to assure that the prediction is based on correct set of assumptions and conform to accepted domain expertise knowledge. A proper set of questions to the experts can include inquiries such as where the information comes from, why certain information is pertinent, what is the relationship of components and also would several experts agree on such an assignment. Among many, one of the main concerns is the trustworthiness of applying AI technologies There are limitations of current continuing education approaches, and we suggest improvements that can help in such transformation. It takes an intersection of human judgment and the power of computer technology to make a step-change in accepting predictions by (ML) machine learning. A deep understanding of the problem, coupled with an awareness of the key data, is always the starting point. The best solution strategy in petroleum engineering adaptation of digital technologies requires effective participation of the domain experts in algorithmic-based preprocessing of data. Application of various digital solutions and technologies can then be tested to select the best solution strategies. For illustration purposes, we examine a few examples where digital technologies have significant potentials. Yet in all, domain expertise and data preprocessing are essential for quality control purposes

Prediction of Fluid Viscosity in Multiphase Reservoir Oil System by Machine Learning

It is important to realize rapid and accurate prediction of fluid viscosity in a multiphase reservoir oil system for improving oil production in petroleum engineering. This study proposed three viscosity prediction models based on machine learning approaches. The prediction accuracy comparison results show that the random forest (RF) model performs accurately in predicting the viscosity of each phase of the reservoir, with the lowest error percentage and highest R 2 values. And the RF model is tremendously fast in a computing time of 0.53 s. In addition, sensitivity analysis indicates that for a multiphase reservoir system, the viscosity of each phase of the reservoir is determined by different factors. Among them, the viscosity of oil is vital for oil production, which is mainly affected by the molar ratio of gas to oil (MR-GO).

Unlocking the Potential of Electrical Submersible Pumps: The Successful Testing and Deployment of a Real-Time Artificially Intelligent System, for Failure Prediction, Run Life Extension, and Production Optimization

This paper is a summary of the collaborative work between a Gulf Cooperation Council (GCC) national oil company (NOC) and Nybl, a deep tech development company, and the results of applying Nybl's proprietary science-based AI to the GCC NOC ESP wells in real-time applications. The paper demonstrates the potential benefits of the real-life application of AI / Machine Learning in conjunction with traditional Petroleum Engineering concepts and algorithms to predict imminent and future failures, extend and monitor run life, and maximize the production of Electrical Submersible Pumps (ESP's). This paper will highlight the NOC's innovative approach to pilot new technology through successful deployment on 27 wells, spread onshore and offshore, in real-time, with prescriptive actions.

Digital-Twin for Production Monitoring and Optimisation: Two Case Study Application Examples

Well network simulation and optimization is an established technology within BP for production optimization. However, for simplicity, the processing facilities are usually only considered as fixed oil, gas and water flow rate constraints. Actual production limits vary as a function of operating conditions and/or cannot be measured directly (e.g. True Vapour Pressure (TVP) or gas velocity at the inlet separator nozzles). To improve on existing workflows, BP has expanded its existing petroleum engineering-focused toolkit and is now globally deploying an end-to-end production system digital twin that extends from the well choke to the facility export for system surveillance and optimization. The end-to-end production system digital twin is a cloud-based system that links sensor data from the asset historian with an equipment data model and third-party first principle steady state simulation tools for an accurate representation of the well network and processing facilities. It supports multi-discipline collaboration, particularly between Petroleum Engineers and Process Engineers, and is remotely accessible by a globally dispersed team. This integrated digital twin can be used in two modes: monitoring and what-if. In monitoring mode, the models are automatically updated hourly with real time data and key simulation results extracted and stored. These monitoring simulations generate virtual sensor output, providing insights that cannot be measured by real sensors. In what-if mode, engineers test scenarios risk-free to explore optimization opportunities. As well as routine optimizations to align with production forecast updates, this can also include scenarios during planned abnormal operations (e.g. facility equipment offline for maintenance or well flowback). An early pilot in a key production region delivered significant production upside and was foundational for the subsequent global roll-out program. This paper will illustrate two practical applications from early deployment activities: (1) condensate recovery optimization (2) well routing optimization / feasibility against variable processing facility limits.

Petroleum Development Oman Forecasting Management System

Production performance forecasting is considered as one of the most challenging and time consuming tasks in petroleum engineering disciplines, it has important implications on decision-making, planning production and processing of facilities. In Petroleum Development Oman (PDO), which is the major petroleum company in Oman, production forecast provides a technical input basis for the economic decisions throughout the exploration and production lifecycle. Reservoir engineers spend more than 250 days per year to complete this process. PDO Forecast Management System (FMS) was introduced to transform the conventional forecasting of gas production. Employing the latest state-of-the-art technologies in the field of data management and machine learning (ML), PDO FMS aims at optimizing and automating the process of capturing, reporting, and predicting hydrocarbon production. This new system covers the full forecast processes including long and short-term forecasting for gas, condensate, and water production. As a pilot project, PDO FMS was deployed on a cluster of 272 wells and relied on agile project management approach to realize the benefits during the development phase. Deployment of the new system resulted in a significant reduction of the forecasting time, optimization of manpower and forecasting accuracy.

20 Years of Horizontal Multistage Completions: A Summary of Industry Evolution in Unconventional and Conventional Plays

In the past two decades, the advent of the Shale Gas Revolution (SGR) was made possible by the visionary idea that hydrocarbons contained in ultra-low permeability source rocks could be extracted using available technology. Usually, these hydrocarbons take geological time to migrate to higher permeability reservoir rocks until the right structural conditions evolve to extract as recoverable resources. However, paradigm shifts in drilling and completion engineering have enabled unlocking resources from these ultra-tight formations. The innovative idea at the base of this industrial revolution was the combination of horizontal well drilling and hydraulic fracturing, which allowed increasing the surface area available for hydrocarbon flow and overcame the slow and shallow hydrocarbon release from the source rock. This approach can be considered as a bridge between petroleum engineering based on radial diffusivity equation and mining engineering based on physically accessing and extracting the resource. To achieve the high number of hydraulic fractures needed for economical production, different execution techniques evolved and developed in what is known as horizontal multistage fracturing (HMSF) completions. Although HMSF is indescribably linked to SGR, it was surprisingly applied in tight gas formation and offshore sand control applications more than 30 or 40 years ago. SGR contributed to the fast development of new innovative systems engineered and deployed at scale all over North America land operations and was subsequently exported internationally in conventional, unconventional, land, and offshore applications. This paper will cover the most common HMSF completion systems types with a primary focus on unconventionals. It will encompass the evolution of these systems over the past several decades. It will also explore the opportunity case for conventional, and high permeability plays through a series of theoretical and real examples.

Expert Advisory System for Production Surveillance and Optimization Assisted by Artificial Intelligence

Petroleum Engineers are usually responsible for 50-200 wells. The wells in highly instrumented fields generate 10-20 measurements every few seconds. This makes it difficult to be on top of every well, every day. This challenge carries a significant opportunity cost, therefore the surveillance process requires automation by implementing surveillance-by-exception. Faster identification of problems is great, but not enough unless the required activities are executed in a timely manner. The ability to execute quickly and safely requires a well-structured coordination effort between the different disciplines involved in field operations. In line with ADNOC Digital Transformation strategy, the solution described in this paper intends to couple surveillance by exception (a Petroleum Engineering workflow) with field operations execution (a multi-disciplinary set of workflows in the field). The integration is achieved by creating a simple yet robust action tracking system, and feeding it automatically with new opportunities, so that it is kept up to date. Automatic diagnosis becomes opportunities. Opportunities become activities. Activities are assigned, executed and closed. All activities are tracked on a high level, which provides insights and visibility to all parties on who is doing what, when and how to close the opportunity. The surveillance by exception engine consumes real time measurements from the historian. It then runs a set of soft sensors using full physics, reduced order models, proxy and data driven machine learning models, which utilize most of the measurements. The measured and calculated values are then fed to an expert system, which automatically diagnoses the wells and creates tickets with recommendations to the production engineer. The engineer reviews the ticket and forwards to field operations for execution. The log of activities enables a direct measure of operational effectiveness. This paper describes the philosophy of the system, how it works, lessons learned and the results of implementation across 6 oilfields and 600+ wells in Abu Dhabi.