Advancement in Digital Oil Field Technology: Maximizing Production and Improving Operational Efficiency through Data-Driven Technologies

2018 ◽  
pp. 137-143
Author(s):  
Richard Mohan David
Keyword(s):  
Operational Efficiency ◽  
Oil Field ◽  
Data Driven

Friction Torque and Leakage Based Data-Driven Approach for Rotary Seal Prognostics in Manufacturing Industry

2019 ◽  
Author(s):  
Madhumitha Ramachandran ◽  
Zahed Siddique
Keyword(s):  
Manufacturing Industry ◽  
Friction Torque ◽  
Operating Conditions ◽  
Oil Field ◽  
Data Driven ◽  
Sensor Technology ◽  
Physical Parameters ◽  
Direct Estimation ◽  
Rotary Seal ◽  
Wide Range

Abstract Rotary seals are found in many manufacturing equipment and machines used for various applications under a wide range of operating conditions. Rotary seal failure can be catastrophic and can lead to costly downtime and large expenses; so it is extremely important to assess the degradation of rotary seal to avoid fatal breakdown of machineries. Physics-based rotary seal prognostics require direct estimation of different physical parameters to assess the degradation of seals. Data-driven prognostics utilizing sensor technology and computational capabilities can aid in the in-direct estimation of rotary seals’ running condition unlike the physics-based approach. An important aspect of data-driven prognostics is to collect appropriate data in order to reduce the cost and time associated with the data collection, storage and computation. Seals in machineries operate in harsh conditions, especially in the oil field, seals are exposed to harsh environment and aggressive fluids which gradually reduces the elastic modulus and hardness of seals, resulting in lower friction torque and excessive leakage. Therefore, in this study we implement a data-driven prognostics approach which utilizes friction torque and leakage signals along with Multilayer Perceptron as a classifier to compare the performance of the two metrics in classifying the running condition of rotary seals. Friction torque was found to have a better performance than leakage in terms of differentiating the running condition of rotary seals throughout its service life. Although this approach was designed for seals in oil and gas industry, this approach can be implemented in any manufacturing industry with similar applications.

Using Marketing Technology in Reservoir Engineering: The Application of Data Driven Predictive Analysis on a Mature Oil Field

2011 ◽  
Author(s):  
Ellen Bertina Zijlstra ◽  
Gael Riethmuller ◽  
Susanne Schaeftlein ◽  
Salima Saif Al Mahruqi ◽  
Ali Naamani ◽  
...  
Keyword(s):  
Oil Field ◽  
Data Driven ◽  
Reservoir Engineering ◽  
Predictive Analysis ◽  
Marketing Technology

Integrated Automation and Data-Driven Workflow for CO2 Project Management – Case Study from a Smart Oil Field in the Middle-East

2021 ◽  
Author(s):  
Erismar Rubio ◽  
Mohamed Yousef Alklih ◽  
Nagaraju Reddicharla ◽  
Abobaker Albelazi ◽  
Melike Dilsiz ◽  
...  
Keyword(s):  
Project Management ◽  
Real Time ◽  
Reservoir Management ◽  
Oil Field ◽  
Data Driven ◽  
Co2 Injection ◽  
Well Performance ◽  
Potential Cost ◽  
Integrated Automation ◽  
Well Pattern

Abstract Automation and data-driven models have been proven to yield commercial success in several oil fields worldwide with reported technical advantages related to improved reservoir management. This paper demonstrates the implementation of an integrated workflow to enhance CO2 injection project performance in a giant onshore smart oil field in Abu Dhabi. Since commissioning, proactive evaluation of the reservoir management strategy is enabled via smart-exception-based surveillance routines that facilitate reservoir/pattern/well performance review and supporting the decision making process. Prolonging the production sustainability of each well is a key pillar of this work, which has been made more quantifiable using live-tracking of the produced CO2 content and corrosion indicators. The intensive computing technical tasks and data aggregation from different sources; such as well testing and real time production/injection measurements; are integrated in an automatic workflow in a single platform. Accordingly, real-time visualizations and dashboards are also generated automatically; to orchestrate information, models and multidisciplinary knowledge in a systematic and efficient manner; allowing engineers to focus on problematic wells and giving attention to opportunity generation in a timely manner. Complemented with numerical techniques and other decision support tools, the intelligent system data-driven model assist to obtain a reliable short-term forecast in a shorter time and help making quick decisions on day-to-day operational optimization aspects. These dashboards have allowed measuring the true well/pattern performance towards operational objectives and production targets. A complete set of KPI's has helped to identify well health-status, potential risks and thus mitigate them for short/long term recovery to obtain an optimum reservoir energy balance in daily bases. In case of unexpected well performance behaviors, the dashboards have provided data insights on the root causes of different well issues and thus remedial actions were proposed accordingly. Maintaining CO2 miscibility is also ensured by having the right pressure support around producers, taking proactive actions from continues evaluation of producer-injector connectivity/interdependency, improving injection/production schedule, validating/tuning streamline model based on surveillance insights, avoiding CO2 recycling, optimizing data acquisition plan with potential cost saving while taking preventive measures to minimize well/facility corrosion impact. In this work, best reservoir management practices have been implemented to create a value of 12% incremental oil recovery from the field. The applied methodology uses an integrated automation and data-driven modeling approach to tackle CO2 injection project management challenges in real-time.

Using Marketing Technology in Reservoir Engineering: The Application of Data Driven Predictive Analysis on a Mature Oil Field

2011 ◽  
Author(s):  
Ellen Bertina Zijlstra ◽  
Gael Riethmuller ◽  
Susanne Schaeftlein ◽  
Salima Saif Al Mahruqi ◽  
Ali Naamani ◽  
...  
Keyword(s):  
Oil Field ◽  
Data Driven ◽  
Reservoir Engineering ◽  
Predictive Analysis ◽  
Marketing Technology

Rapid and Efficient Waterflood Optimization Using Augmented AI Approach in a Complex Offshore Field

2021 ◽  
Author(s):  
Shawket Ghedan ◽  
Meher Surendra ◽  
Agustin Maqui ◽  
Mahmoud Elwan ◽  
Rami Kansao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Oil Field ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Water Flooding ◽  
Fault System ◽  
Fractional Flow ◽  
Operational Decisions ◽  
Water Cut ◽  
Offshore Field

Abstract Waterfloods are amongst the most widely implemented methods for oil field development. Despite their vast implementation, operational bottlenecks such as lack of surveillance and optimization tools to guide fast paced decisions render most of these sub-optimal. This paper presents a novel machine-learning, reduced-physics approach to optimize an exceptionally complex off-shore waterflood in the Gulf of Suez. Leveraging a hybrid data-driven and physics approach, the water flooding scheme in Nezzezat reservoir was optimized to improve reservoir voidage replacement, increase oil production, and reduce water production by identifying potential in wells. As a by-product of the study, a better understanding of the complex fault system was also achieved. Including the geological understanding and its uncertainty is one of the key elements that must be preserved. All geological attributes, along with production rates are used to solve for pressure and inter-well communication. This is later supplemented by machine-learning algorithm to solve for the fractional flow of inter-well connections. Combining the inter-well connectivity and fractional flow, an optimization was performed to reach the best possible conditions for oil gains and water-cut reduction. A global optimization is possible thanks to the low computational demand of this approach, as thousands to millions of realizations must be run to reach the best solution while satisfying all constraints. This is all done in a fraction of the time it takes to run a traditional reservoir simulation. For the present case, the paper will present the underlying physics and data-driven algorithms, along with the blind tests performed to validate the results. In addition to the method's inner workings, the paper will focus more on the results to guide operational decisions. This is inclusive of all the complex constraints of an offshore field, as well as the best reservoir management practices, when reaching optimal production and injection rates for each well. An increase in production was achieved with some reduction in water-cut, while honoring well and platform level limitations. While these represent the gains for a particular month, optimization scenarios can be run weekly or monthly to capture the dynamic nature of the problem and any operational limitations that might arise. The ability to update the models and run optimization scenarios effortlessly allows pro-active operational decisions to maximize the value of the asset. The approach followed in this paper solves for the critical physics of the problem and supplements the remaining with machine learning algorithms. This novel and extremely practical approach facilitate the decision making to operate the field optimally.

Probabilistic Data-Driven Prediction of Wellbore Signatures in High-Dimensional Data Using Bayesian Networks

SPE Journal ◽  
2018 ◽  
Vol 23 (04) ◽  
pp. 1090-1104 ◽  
Author(s):  
Nastaran Bassamzadeh ◽  
Roger Ghanem
Keyword(s):  
Production Rate ◽  
Oil Field ◽  
Assessment Tools ◽  
Data Driven ◽  
High Dimensional ◽  
Dimensional System ◽  
Probabilistic Prediction ◽  
Modeling Framework ◽  
Data Set ◽  
Local Linear Embedding

Summary Accurate, data-driven, stochastic models for fluid-flow prediction in hydrocarbon reservoirs are of particular interest to reservoir engineers. Being computationally less costly than conventional physical simulations, such predictive models can serve as rapid-risk-assessment tools. In this research, we seek to probabilistically predict the oil-production rate at locations where limited data are observed using the available data at other spatial points in the oil field. To do so, we use the Bayesian network (BN), which is a modeling framework for capturing dependencies between uncertain variables in a high-dimensional system. The model is applied to a real data set from the Gulf of Mexico (GOM) and it is shown that BN is able to predict the production rate with 86% accuracy. The results are compared with neural-network and co-Kriging methods. Moreover, BN structure enables us to select the most-relevant variables for prediction, and thus we managed to reduce the input dimension from 36 to 17 variables while preserving the same prediction accuracy. Similarly, we use the local-linear-embedding (LLE) method as a feature-extraction tool to nonlinearly reduce the input dimension from 36 to 10 variables with negligible loss in accuracy. Accordingly, we claim that BN is a valuable modeling tool that can be efficiently used for probabilistic prediction and dimension reduction in the oil industry.

scholarly journals Data-driven optimization for fine water injection in a mature oil field

2020 ◽  
Vol 47 (3) ◽  
pp. 674-682
Author(s):  
Deli JIA ◽  
He LIU ◽  
Jiqun ZHANG ◽  
Bin GONG ◽  
Xiaohan PEI ◽  
...  
Keyword(s):  
Water Injection ◽  
Oil Field ◽  
Data Driven

A Data-Driven Approach to Evaluate Fracturing Practice in Tight Sandstone in Changqing Field

2021 ◽  
Author(s):  
Tao Wu ◽  
Hanzhi Fang ◽  
Hu Sun ◽  
Feifei Zhang ◽  
Xi Wang ◽  
...  
Keyword(s):  
Oil Production ◽  
Gas Production ◽  
Oil Field ◽  
Data Driven ◽  
Data Sets ◽  
Effective Parameters ◽  
Tight Sandstone ◽  
Well Productivity ◽  
Input Parameters ◽  
Data Driven Approach

Abstract Unconventional reservoirs such as shale and tight sandstones that with ultra-low permeability, are becoming increasingly significant in global energy structures (Pejman T, et al., 2017). For these reservoirs, successful hydraulic fracturing is the key to extract the hydrocarbon resources efficiently and economically. However, the intrinsic mechanisms of fracturing growth in the tight formations are still unclear. In practice, fracturing design mainly depends on hypothetical models and previous experience, which leads to difficulties in evaluating the performance of the fracturing jobs. Therefore, an improved method to optimize parameters for fracturing is necessary and beneficial to the industry. In this paper, a data-driven approach is used to evaluate the factors that dominate the production rate from tight sandstone formation in Changqing Field which is the largest oil field in China. In the model, the input parameters are classified into two categories: controllable parameters (e.g. stage numbers, fracturing fluid volume) and uncontrollable parameters (e.g. formation properties), and the output parameter is the accumulated oil production of the wells. Data for more than 100 wells from different formations and zones in Changqing Field are collected for this study. First, a stepwise data mining method is used to identify the correlations between the target parameter and all the available input parameters. Then, a machine learning model is developed to predict the well productivity for a given set of input parameters accurately. The model is validated by using separate data-sets from the same field. An optimize algorithm is combined with the data-driven model to maximize the cumulative oil production for wells by tuning the controllable parameters, which provides the optimized fracturing design. By using the developed model, low productivity wells are identified and new fracturing designs are recommended to improve the well productivity. This paper is useful for understanding the effects of designed fracturing parameters on well productivity in Changqing Oilfield. Furthermore, it can be extended to other unconventional oil fields by training the model with according data sets. The method helps operators to select more effective parameters for fracturing design, and therefore reduce the operation costs for fracturing and improve the oil and gas production.

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