Hybrid data driven drilling and rate of penetration optimization

Alali, Ammar; Abughaban, Mahmoud; Aman, Beshir; Ravela, Sai

doi:10.1016/j.petrol.2020.108075

Cited by 31 publications

(4 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other parameters such as the torque, standpipe pressure (SPP), mud weight, and mud rheological properties also play a critical role in achieving a better ROP. However, these parameters cannot be manipulated in realtime, since they are a function of the formation type, lithology, and temperature, among others [51].…”

Section: Theory Of Pre-operational Testingmentioning

confidence: 99%

Analysis and Multi-Objective Optimization of the Rate of Penetration and Mechanical Specific Energy: A Case Study Applied to a Carbonate Hard Rock Reservoir Based on a Drill Rate Test Using Play-Back Methodology

Mantegazini,

Nascimento,

Dornelas

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Until early 2006, in Brazil, the focus used to be on oil and gas exploration/exploitation of post-salt carbonates. This changed when the industry announced the existence of large fields in pre-salt layers across the South Atlantic Ocean from nearshore zones up to almost 350 [km] from the shore. With the discovery of pre-salt hydrocarbons reservoirs, new challenges appeared. One of the main challenges is the necessity to optimize the drilling processes due to their high operational costs. Drilling costs are considerably high, which leads the oil and gas industry to search for innovative and entrepreneurial methods. The coupling of the mechanical specific energy (MSE) and the rate of penetration (ROP) is a method that allows for the identification of ideal conditions to efficiently enhance the drilling process. In addition, the performance of the drilling process can be estimated through pre-operational tests, which consist in continuously testing the applied drilling mechanic parameters, such as the weight-on-bit (WOB) and drill string rotary speed (RPM), looking for optimum sets that would ultimately provide the most desirable ROP. Thus, the goal of this research was to analyze field data from pre-salt layer operations, using a multi-objective optimization based on the play-back methodology for pre-operational drilling tests, through the ideal combination of the highest ROP and the lowest MSE. The results showed that the new concept of pre-operational tests based on the MSE proved to be effective in the drilling process optimization. The combination of the highest ROP and the lowest MSE allows for a high-performance drilling process. For WOB intervals of 5 and 7 [klb], a good fit of the parameters was obtained. Through the parameters obtained from pre-operational tests, the eventual cost-saving and time-saving values could be estimated, respectively, ranging from USD 1,056,180 to 1,151,898 and 19.50 to 21.27 [h], respectively. In addition, the results of this research can be applied to the exploration of other natural resources, such as natural hydrogen and geothermal sources.

show abstract

Section: Theory Of Pre-operational Testingmentioning

confidence: 99%

Analysis and Multi-Objective Optimization of the Rate of Penetration and Mechanical Specific Energy: A Case Study Applied to a Carbonate Hard Rock Reservoir Based on a Drill Rate Test Using Play-Back Methodology

Mantegazini,

Nascimento,

Dornelas

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…WOB, RPM, and flow rate all need to be considered as characteristic variables. Torque and standpipe pressure are state variables of drilling efficiency that cannot be directly manipulated during drilling and are not considered characteristic variables [30]. According to previous studies, ROP is not only related to drilling parameters but also influenced by rock strength [31].…”

Section: Feature Selectionmentioning

confidence: 99%

Data Knowledge Dual-Driven Rate of Penetration Prediction Method for Horizontal Wells

Zhao,

Yin,

2023

Applied Sciences

View full text Add to dashboard Cite

The prediction and optimization of the rate of penetration (ROP) for horizontal wells are more complicated than for vertical wells, but most of the current ROP prediction studies are for vertical wells, which cannot be adapted to the complex drilling characteristics of horizontal wells. To this end, this paper proposes a data knowledge dual-driven horizontal well ROP prediction method. Firstly, the drilling characteristics of horizontal wells are analyzed, showing that the horizontal wells ROP prediction model cannot be modeled using surface measurement data; secondly, based on the analysis of horizontal well drilling characteristics, a physical model-based horizontal well ROP modeling data pre-processing method is proposed by introducing the drag and torque model. Finally, a data knowledge dual-driven horizontal well ROP prediction method is proposed in conjunction with data-driven algorithms. The proposed horizontal well ROP prediction method is applied to the A1~A4 wells in the Sichuan area. Compared with the conventional data-driven ROP prediction method, the prediction accuracy of this method is improved by 30%. The proposed method can provide a basis for the intelligent optimization and management of ROP during the drilling of horizontal wells.

show abstract

“…This gives rise to the attention paid to the various data-driven models as an alternative in lieu of a numerical simulator. With higher speed and efficiency, datadriven models have been widely applied in the oil and gas industry, including for exploration, [7] drilling, [8,9] completion, [10] and production. [11] In particular, many studies utilized a series of data-driven models based on different techniques to analyze the SAGD process.…”

Section: Introductionmentioning

confidence: 99%

Integration of machine learning and data analysis for the SAGD production performance with infill wells

2023

View full text Add to dashboard Cite

There have been numerous studies on predicting the production performance of the steam assisted gravity drainage (SAGD) process by data‐driven models with different machine learning algorithms since their introduction into industry. Similar efforts on SAGD infill wells, nevertheless, remain rare for this advanced alteration in improving the classical SAGD performance. On the other hand, predictive tools to optimize an infill well start time is useful in maximizing bitumen production and minimizing its costs. In this paper, a series of SAGD infill well models are constructed with selected ranges of operational conditions. Three SAGD infill well production performance indicators, namely, an increased ratio (), a total steam–oil ratio (SORtotal), and a stolen ratio () for each SAGD infill well, are calculated based on simulated infill well cases and control models. Five different machine learning algorithms (an artificial neural network [ANN] algorithm, three gradient boosting decision tree [GBDT] algorithms, and a support vector machine [SVM] algorithm) are trained, tested, and evaluated for their effectiveness in predicting those three indicators as output parameters, given seven SAGD relevant parameters as input parameters. Comparisons of different data sets show that the ANN is the best in predicting all three performance indicators under different infill well start times among all the above machine learning algorithms, while the GBDT algorithms have a better ability to learn a variation trend in the SAGD infill well performance.

show abstract

Hybrid data driven drilling and rate of penetration optimization

Cited by 31 publications

References 23 publications

Analysis and Multi-Objective Optimization of the Rate of Penetration and Mechanical Specific Energy: A Case Study Applied to a Carbonate Hard Rock Reservoir Based on a Drill Rate Test Using Play-Back Methodology

Analysis and Multi-Objective Optimization of the Rate of Penetration and Mechanical Specific Energy: A Case Study Applied to a Carbonate Hard Rock Reservoir Based on a Drill Rate Test Using Play-Back Methodology

Data Knowledge Dual-Driven Rate of Penetration Prediction Method for Horizontal Wells

Integration of machine learning and data analysis for the SAGD production performance with infill wells

Contact Info

Product

Resources

About