Prediction of Well Production Event Using Machine Learning Algorithms

Alatrach, Yara; Mata, Carlos; Omrani, P. Shoeibi; Saputelli, Luigi; Narayanan, Ram; Hamdan, Mohammad

doi:10.2118/202961-ms

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…The application of machine learning strategies has been widely practiced in the oil and gas development. These attempts have covered aspects of enhanced oil recovery [7][8][9][10][11][12][13][14], fracture detection [15], development plan optimization [15,16], dynamic production prediction [18][19][20][21] and asphaltene precipitation prediction [22]. Some studies have also focused on applying machine learning strategies to model permeability impairment due to mineral scale deposition [23][24][25] and predict the success of an inhibition scenario in the field [4].…”

Section: Introductionmentioning

confidence: 99%

A Data Mining Perspective on the Confluent Ions` Effect for Target Functionality

Fazelabdolabadi

Montazeri

Pourafshary

2021

HighTech. Innov. J.

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The production of hydrocarbon resources at an oil field is concomitant with challenges with respect to the formation of scale inside the reservoir rock – intricately impairing its permeability and hindering the flow. Historically, the effect of ions is attributed to the undergone phenomenon; nevertheless, there exists a great deal of ambiguity about its relative significance compared to other factors, or the effectiveness as per the ion type. The present work applies a data mining strategy to unveil the influencing hierarchy of the parameters involved in driving the process within major rock categories – sandstone and carbonate – to regulate a target functionality. The functionalities considered evolve around maximizing the oil recovery, minimizing permeability impairment/ scale damage. A pool of experimental as well as field data was used for this sake, accumulating the bulk of the available literature data. The methods used for data analysis in the present work included the Bayesian Network, Random Forest, Deep Neural Network, as well as Recursive Partitioning. The results indicate a rolling importance for different ion species - altering under each functionality – which is not ranked as the most influential parameter in either case. For the oil recovery target, our results quantify a distinction between the source of ion of a single type, in terms of its influencing rank in the process. This latter deduction is the first proposal of its kind – suggesting a new perspective for research. Moreover, the machine learning methodology was found to be capable of reliably capturing the data – evidenced by the minimal errors in the bootstrapped results. Doi: 10.28991/HIJ-2021-02-03-05 Full Text: PDF

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Section: Introductionmentioning

confidence: 99%

A Data Mining Perspective on the Confluent Ions` Effect for Target Functionality

Fazelabdolabadi

Montazeri

Pourafshary

2021

HighTech. Innov. J.

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Generating extremely low-dimensional representation of subsurface earth models using vector quantization and deep Autoencoder

Falola,

Churilova,

Liu

et al. 2024

Petroleum Research

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

Mata

Saputelli

Mohan

et al. 2021

Day 1 Mon, November 15, 2021

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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.

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Prediction of Well Production Event Using Machine Learning Algorithms

Cited by 4 publications

References 18 publications

A Data Mining Perspective on the Confluent Ions` Effect for Target Functionality

A Data Mining Perspective on the Confluent Ions` Effect for Target Functionality

Generating extremely low-dimensional representation of subsurface earth models using vector quantization and deep Autoencoder

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

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