Impact of Digitalization on the Way of Working and Skills Development in Hydrocarbon Production Forecasting and Project Decision Analysis

Clemens, Torsten; Viechtbauer-Gruber, Margit

doi:10.2118/200540-ms

Cited by 4 publications

(1 citation statement)

References 86 publications

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“…In oil and gas sector, digital transformation is an emerging driver which can play a pivotal role in the industry, redefining its boundaries [6]. Among the applications made possible by digital transformation, we can mention [7][8][9] the following:…”

Section: Digital Transformationmentioning

confidence: 99%

Digital transformation: a review on artificial intelligence techniques in drilling and production applications

D'Almeida

Bergiante

Ferreira

et al. 2022

Int J Adv Manuf Technol

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The use of digital and artificial intelligence technologies has expanded and influenced business models and the opening of opportunities for the generation of value in several organizations, in a movement known as industry 4.0. The oil industry has been following the path of several other industrial sectors and has implemented digitalization to solve different challenges and problems. The present work, supported by extensive research carried out in the specialized literature, shows relevant applications of digital transformation to solve problems in drilling and production operations during the life cycle of an oil well. The main issues addressed in the research were stuck pipes and hydrate formation. The achievements show that control systems and the various sensors used during drilling and the useful life of an oil well generate data that creates opportunities for the use of computational and artificial intelligence techniques. New technologies associated with digital transformation include smart surveillance systems, real-time monitoring, and intelligent equipment. In a well oil environment, these novelties are associated with fault detection and prediction systems to avoid or reduce problems or accidents that may cause costs or, in extreme cases, lead to the loss of the well. The study also points out that the oil industries, research centers, and universities are increasingly working together to understand the challenges and overcome the problems associated with the implementation and greater use of digital transformation technologies.

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Section: Digital Transformationmentioning

confidence: 99%

Digital transformation: a review on artificial intelligence techniques in drilling and production applications

D'Almeida

Bergiante

Ferreira

et al. 2022

Int J Adv Manuf Technol

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Hydrocarbon Field (Re-)Development as Markov Decision Process

Sieberer

Clemens²

2022

SPE Reservoir Evaluation &Amp; Engineering

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Summary Hydrocarbon field (re-)development requires that a multitude of decisions are made under uncertainty. These decisions include the type and size of surface facilities, location, configuration, and number of wells but also which data to acquire. Both types of decisions, which development to choose and which data to acquire, are strongly coupled. The aim of appraisal is to maximize value while minimizing data acquisition costs. These decisions have to be done under uncertainty owing to the inherent uncertainty of the subsurface but also of other costs and economic parameters. Conventional value of information (VOI) evaluations can be used to determine how much can be spent to acquire data. However, VOI is very challenging to calculate for complex sequences of decisions with various costs and including the risk attitude of the decision-maker. We are using a partially observable Markov decision process (MDP) to determine the policy for the sequence and type of measurements and decisions to do. A partially observable MDP (POMDP) is characterized by the states (here: description of the system at a certain point in time), actions (here: measurements and development scenario), transition function (probabilities of transitioning from one state to the next), observations (measurements, appraisal activity), and rewards [costs for measurements, expected monetary value (EMV) of development options]. Solving the MDP gives the optimum policy, sequence of the decisions, the probability of maturation (POM) of a project, the EMV, the expected loss, the expected appraisal costs, and the probability of economic success (PES). These key performance indicators can then be used to select in a portfolio of projects the ones generating the highest expected reward for the company. Combining the production forecasts from numerical model ensembles with probabilistic capital and operating expenditures and economic parameters allows for quantitative decision-making under uncertainty.

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Production Forecasting Based on Attribute-Augmented Spatiotemporal Graph Convolutional Network for a Typical Carbonate Reservoir in the Middle East

Gao

Wei

Zhao³

et al. 2022

Energies

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Production forecasting plays an important role in development plans during the entire period of petroleum exploration and development. Artificial intelligence has been extensively investigated in recent years because of its capacity to extensively analyze and interpret complex data. With the emergence of spatiotemporal models that can integrate graph convolutional networks (GCN) and recurrent neural networks (RNN), it is now possible to achieve multi-well production prediction by considering the impact of interactions between producers and historical production data simultaneously. Moreover, an accurate prediction not only depends on historical production data but also on the influence of neighboring injectors’ historical gas injection rate (GIR). Therefore, based on the assumption that introducing GIR can enhance prediction accuracy, this paper proposes a deep learning-based hybrid production forecasting model that is aimed at considering both the spatiotemporal characteristics of producers and the GIR of neighboring injectors. Specifically, we integrated spatiotemporal characteristics and GIR into an attribute-augmented spatiotemporal graph convolutional network (AST-GCN) and gated recurrent units (GRU) neural network to extract intricate temporal correlations from historical data. The method proposed in this paper has been successfully applied in a well pattern (including five producers and seven gas injectors) in a low-permeability carbonate reservoir in the Middle East. In single well production forecasting, the error of AST-GCN is 63.2%, 37.3%, and 16.1% lower in MedAE, MAE, and RMSE compared with GRU and 62.9%, 44.6%, and 28.9% lower compared with RNS. Similarly, the accuracy of AST-GCN is 15.9% and 35.8% higher than GRU and RNS in single well prediction. In well-pattern production forecasting, the error of AST-GCN is 41.2%, 64.2%, and 75.2% lower in RMSE, MAE, and MedAE compared with RNS, while the accuracy of AST-GCN is 29.3% higher. After different degrees of Gaussian noise are added to the actual data, the average change in AST-GCN is 3.3%, 0.4%, and 1.2% in MedAE, MAE, and RMSE, which indicates the robustness of the proposed model. The results show that the proposed model can consider the production data, gas injection data, and spatial correlation at the same time, which performs well in oil production forecasts.

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Impact of Digitalization on the Way of Working and Skills Development in Hydrocarbon Production Forecasting and Project Decision Analysis

Cited by 4 publications

References 86 publications

Digital transformation: a review on artificial intelligence techniques in drilling and production applications

Digital transformation: a review on artificial intelligence techniques in drilling and production applications

Hydrocarbon Field (Re-)Development as Markov Decision Process

Production Forecasting Based on Attribute-Augmented Spatiotemporal Graph Convolutional Network for a Typical Carbonate Reservoir in the Middle East

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