scholarly journals Comparing ARIMA and LSTM models to predict time series in the oil industry

2021 ◽  
Author(s):  
Jaqueline B. Correia ◽  
Marcos Pivetta ◽  
Givanildo Santana do Nascimento ◽  
Karin Becker

Monitoring and forecasting oil and gas (O\&G) production is essential to extend the life of a well and increase reservoirs' productivity. Popular models for O\&G time series are ARIMA and LSTM recurrent networks, and tipically several lags are forecasted at once. LSTM models can deploy the recursive prediction strategy, which uses one prediction to make the next, or the multiple outputs (MO) strategy, which predicts a sequence of values in a single shot. This work assesses ARIMA and LSTM models for the forecasting of petroleum production time series. We use time series of pressure and gas/oil flow from actual wells with distinct properties, for which we developed predictive models considering different time horizons. For the LSTM models, we deploy both the recursive and MO strategies. Our comparison revealed the superiority of LSTM models in general, and MO-based models for longer time intervals.

2017 ◽  
Vol 4 (1) ◽  
pp. 17-29 ◽  
Author(s):  
Carolina Andrade De Sousa ◽  
Oldrich Joel Romero

Pipelines are highly used in the oil industry for the transportation of oil, natural gas and products from refineries. One of the main areas of study in pipeline engineering is the flow guarantee. This paper describes the steady state dynamics behavior of oil flow in a pipeline with the presence of a leak. The mathematical and numerical modeling of a single-phase flow in a one meter pipeline with a diameter of 0.15 m in an onshore environment. Three different sizes of leaks are studied based on fluid dynamics simulations using the ANSYS FLUENT® 15.0 commercial software. The interest of this paper is to evaluate the influence of leakage on the pressure, velocity fields and flow rates in a 3D section of pipe in order to identify the influence of the perturbation in these parameters. The results obtained is interpreted in the CFD- Post that is included in the software package. Thus, this work intends to contribute to the development of operations tools in oil and gas industry.


1973 ◽  
Vol 11 (3) ◽  
pp. 480
Author(s):  
J. M. Killey

As onshore oil and gas deposits are becoming more difficult to locate, and as the world demands for energy continue to increase at an alarming rate, oil companies are channeling much of their exploration activities towards offshore operations, and in particular, towards operations centered off Canada's coast lines. Because of the environment, offshore drilling presents problems which are novel to the onshore-geared oil industry. J. M. Killey discusses in detail many of the considerations involved in drafting the offshore drilling contract, concentrating on problems such as the liability of the various parties; costs; scheduling; pollution; conflict of laws; etc. Similarly, he discusses service contracts (such as supply boat charters; towing services; helicopter services; etc.^ which are necessity to the operation of an offshore drilling rig. To complement his paper, the author has included number of appendices which list the various considerations lawyer must keep in mind when drafting contracts for offshore operations.


2021 ◽  
Author(s):  
Nouf AlJabri ◽  
Nan Shi

Abstract Nanoemulsions (NEs) are kinetically stable emulsions with droplet size on the order of 100 nm. Many unique properties of NEs, such as stability and rheology, have attracted considerable attention in the oil industry. Here, we review applications and studies of NEs for major upstream operations, highlighting useful properties of NEs, synthesis to render these properties, and techniques to characterize them. We identify specific challenges associated with large-scale applications of NEs and directions for future studies. We first summarize useful and unique properties of NEs, mostly arising from the small droplet size. Then, we compare different methods to prepare NEs based on the magnitude of input energy, i.e., low-energy and high-energy methods. In addition, we review techniques to characterize properties of NEs, such as droplet size, volume fraction of the dispersed phase, and viscosity. Furthermore, we discuss specific applications of NEs in four areas of upstream operations, i.e., enhanced oil recovery, drilling/completion, flow assurance, and stimulation. Finally, we identify challenges to economically tailor NEs with desired properties for large-scale upstream applications and propose possible solutions to some of these challenges. NEs are kinetically stable due to their small droplet size (submicron to 100 nm). Within this size range, the rate of major destabilizing mechanisms, such as coalescence, flocculation, and Ostwald ripening, is considerably slowed down. In addition, small droplet size yields large surface-to-volume ratio, optical transparency, high diffusivity, and controllable rheology. Similar to applications in other fields (food industry, pharmaceuticals, cosmetics, etc.), the oil and gas industry can also benefit from these useful properties of NEs. Proposed functions of NEs include delivering chemicals, conditioning wellbore/reservoir conditions, and improve chemical compatibility. Therefore, we envision NEs as a versatile technology that can be applied in a variety of upstream operations. Upstream operations often target a wide range of physical and chemical conditions and are operated at different time scales. More importantly, these operations typically consume a large amount of materials. These facts not only suggest efforts to rationally engineer properties of NEs in upstream applications, but also manifest the importance to economically optimize such efforts for large-scale operations. We summarize studies and applications of NEs in upstream operations in the oil and gas industry. We review useful properties of NEs that benefit upstream applications as well as techniques to synthesize and characterize NEs. More importantly, we identify challenges and opportunities in engineering NEs for large-scale operations in different upstream applications. This work not only focuses on scientific aspects of synthesizing NEs with desired properties but also emphasizes engineering and economic consideration that is important in the oil industry.


2021 ◽  
Author(s):  
Armstrong Lee Agbaji

Abstract Historically, the oil and gas industry has been slow and extremely cautious to adopt emerging technologies. But in the Age of Artificial Intelligence (AI), the industry has broken from tradition. It has not only embraced AI; it is leading the pack. AI has not only changed what it now means to work in the oil industry, it has changed how companies create, capture, and deliver value. Thanks, or no thanks to automation, traditional oil industry skills and talents are now being threatened, and in most cases, rendered obsolete. Oil and gas industry day-to-day work is progressively gravitating towards software and algorithms, and today’s workers are resigning themselves to the fact that computers and robots will one day "take over" and do much of their work. The adoption of AI and how it might affect career prospects is currently causing a lot of anxiety among industry professionals. This paper details how artificial intelligence, automation, and robotics has redefined what it now means to work in the oil industry, as well as the new challenges and responsibilities that the AI revolution presents. It takes a deep-dive into human-robot interaction, and underscores what AI can, and cannot do. It also identifies several traditional oilfield positions that have become endangered by automation, addresses the premonitions of professionals in these endangered roles, and lays out a roadmap on how to survive and thrive in a digitally transformed world. The future of work is evolving, and new technologies are changing how talent is acquired, developed, and retained. That robots will someday "take our jobs" is not an impossible possibility. It is more of a reality than an exaggeration. Automation in the oil industry has achieved outcomes that go beyond human capabilities. In fact, the odds are overwhelming that AI that functions at a comparable level to humans will soon become ubiquitous in the industry. The big question is: How long will it take? The oil industry of the future will not need large office complexes or a large workforce. Most of the work will be automated. Drilling rigs, production platforms, refineries, and petrochemical plants will not go away, but how work is done at these locations will be totally different. While the industry will never entirely lose its human touch, AI will be the foundation of the workforce of the future. How we react to the AI revolution today will shape the industry for generations to come. What should we do when AI changes our job functions and workforce? Should we be training AI, or should we be training humans?


Significance As in 2020 and 2021, this projected growth will be driven by the ongoing expansion of the oil and gas sector, and related investment and state revenues. These rising revenues will support the government’s ambitious national development plans, which include both increased social and infrastructure spending. Impacts The government will prioritise enhancing the oil and gas investment framework. Investment into joint oil and gas infrastructure with Suriname will benefit the growing oil industry in both countries. The expansionary fiscal policy may lead to a rise in inflation, leading to further calls for wage increases. In the medium term, strong growth in the oil and gas sector could lead to increased climate change activism in the country.


1986 ◽  
Vol 39 (11) ◽  
pp. 1687-1696 ◽  
Author(s):  
Jean-Claude Roegiers

The petroleum industry offers a broad spectrum of problems that falls within the domain of expertise of mechanical engineers. These problems range from the design of well production equipment to the evaluation of formation responses to production and stimulation. This paper briefly describes various aspects and related difficulties with which the oil industry has to deal, from the time the well is spudded until the field is abandoned. It attempts to delineate the problems, to outline the approaches presently used, and to discuss areas where additional research is needed. Areas of current research activity also are described; whenever appropriate, typical or pertinent case histories are used to illustrate a point.


2018 ◽  
Vol 15 (147) ◽  
pp. 20180695 ◽  
Author(s):  
Simone Cenci ◽  
Serguei Saavedra

Biotic interactions are expected to play a major role in shaping the dynamics of ecological systems. Yet, quantifying the effects of biotic interactions has been challenging due to a lack of appropriate methods to extract accurate measurements of interaction parameters from experimental data. One of the main limitations of existing methods is that the parameters inferred from noisy, sparsely sampled, nonlinear data are seldom uniquely identifiable. That is, many different parameters can be compatible with the same dataset and can generalize to independent data equally well. Hence, it is difficult to justify conclusive assertions about the effect of biotic interactions without information about their associated uncertainty. Here, we develop an ensemble method based on model averaging to quantify the uncertainty associated with the effect of biotic interactions on community dynamics from non-equilibrium ecological time-series data. Our method is able to detect the most informative time intervals for each biotic interaction within a multivariate time series and can be easily adapted to different regression schemes. Overall, this novel approach can be used to associate a time-dependent uncertainty with the effect of biotic interactions. Moreover, because we quantify uncertainty with minimal assumptions about the data-generating process, our approach can be applied to any data for which interactions among variables strongly affect the overall dynamics of the system.


2018 ◽  
Vol 7 (1) ◽  
pp. 32
Author(s):  
Ghasem Nikjou ◽  
Hamed Najafi ◽  
Kamran Salmani

Nowadays energy has an important role as a driving sector of economy. Forecasting 150 billion dollars investment in energy sector during the fifth development program in Iran, the banking and financial system require a dynamic and modern economy and financial instruments. Obviously, this approach needs to remove legal barriers and modification of contracts. Financing in the oil industry has faced with serious challenges in recent years. In addition, investing in common offshore oil and gas resources is indispensable. Accordingly we are going to design a new contract which is called Oil SPFO (Standard Parallel Forward security with two Options under betting condition), in order to raise funds needed. In this article we would investigate the SPFO for Iran Ministry of Petroleum (MOP)’s finance and present a model for pricing the oil SPFO based on Black and Scholes option pricing model. Finally, we have some recommendations to develop the oil SPFO and suggest that other researchers work on pricing the oil parallel forward securities according to this model.


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