Gradient boosting to boost the efficiency of hydraulic fracturing

Makhotin, Ivan; Koroteev, Dmitry; Burnaev, Evgeny

doi:10.1007/s13202-019-0636-7

Cited by 31 publications

(11 citation statements)

References 23 publications

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“…In AlAzani et al (2019), cuttings concentration for horizontal and deviated wells was predicted using machine learning. In addition, machine learning approaches were used in many other applications, for instance, mud loss estimation during lost circulation (DunnNorman et al 2018), permeability prediction (Arigbe et al 2018), titration-based asphaltene precipitation (Gholami et al 2015), oil/gas ratio for volatile oil and gas condensate reservoirs (Fattah and Khamis 2018) and hydraulic fracturing prediction (Makhotin et al 2019). In Al-Mudhafar (2017), both machine learning classification and regression approaches were used for lithofacies classification and permeability prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Data-driven approaches tests on a laboratory drilling system

Løken

Løkkevik

Sui

2020

J Petrol Explor Prod Technol

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In recent years, considerable resources have been invested to exploit vast amounts of data that get collected during exploration, drilling and production of oil and gas. Data-related digital technologies potentially become a game changer for the industry in terms of reduced costs through increasing operational efficiency and avoiding accidents, improved health, safety and environment through strengthening situational awareness and so on. Machine learning, an application of artificial intelligence to offer systems/processes self-learning and self-driving ability, has been around for recent decades. In the last five to ten years, the increased computational powers along with heavily digitized control and monitoring systems have made machine learning algorithms more available, powerful and accurate. Considering the state-of-art technologies that exist today and the significant resources that are being invested into the technologies of tomorrow, the idea of intelligent and automated drilling systems to select best decisions or provide good recommendations based on the information available becomes closer to a reality. This study shows the results of our research activity carried out on the topic of drilling automation and digitalization. The main objective is to test the developed machine learning algorithms of formation classification and drilling operations identification on a laboratory drilling system. In this paper, an algorithm to develop data-driven models based on the laboratory data collocated in many scenarios (for instance, drilling different formation samples with varying drilling operational parameters and running different operations) is presented. Moreover, a testing algorithm based on datadriven models for new formation detection and confirmation is proposed. In the case study, results on multiple experiments conducted to test and validate the developed machine learning methods have been illustrated and discussed.

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Section: Literature Reviewmentioning

confidence: 99%

Data-driven approaches tests on a laboratory drilling system

Løken

Løkkevik

Sui

2020

J Petrol Explor Prod Technol

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“…The Gradient Boosting algorithm is often applied for solving different problems connected with oil and gas industry. For example, in paper [28] authors utilized this method for prediction of the flow rate of the well after the process of hydraulic fracturing. In addition, in the article [29] authors created a model based on the Gradient Boosting algorithm for calculation of the bottomhole pressure in the case of transient multiphase flow in wells.…”

Section: Applied Machine Learning Algorithms Their Tuning Andmentioning

confidence: 99%

A predictive model for steady-state multiphase pipe flow: Machine learning on lab data

Kanin

Осипцов

Vainshtein

et al. 2019

Journal of Petroleum Science and Engineering

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Engineering simulators used for steady-state multiphase flows in oil and gas wells and pipelines are commonly utilized to predict pressure drop and phase velocities. Such simulators are typically based on either empirical correlations (e.g., Beggs and Brill, Mukherjee and Brill, Duns and Ros) or first-principles mechanistic models (e.g., Ansari, Xiao, TUFFP Unified, Leda Flow Point model, OLGAS). The simulators allow one to evaluate the pressure drop in a multiphase pipe flow with acceptable accuracy. However, the only shortcoming of these correlations and mechanistic models is their applicability (besides steady-state versions of transient simulators such as Leda Flow and OLGA). Empirical correlations are commonly applicable in their respective ranges of data fitting; and mechanistic models are limited by the applicability of the empirically based closure relations that are a part of such models. In order to extend the applicability and the accuracy of the existing accessible methods, a method of pressure drop calculation in the pipeline is proposed. The method is based on well segmentation and calculation of the pressure gradient in each segment using three surrogate models based on Machine Learning (ML) algorithms trained on a representative lab data set from the open literature. The first model predicts the value of a liquid holdup in the segment, the second one determines the flow pattern, and the third one is used to estimate the pressure gradient. To build these models, several ML algorithms are trained such as Random Forest, Gradient Boosting Decision Trees, Support Vector Machine, and Artificial Neural Network, and their predictive abilities are cross-compared. The proposed method for pressure gradient calculation yields R 2 = 0.95 by using the Gradient Boosting algorithm as compared with R 2 = 0.92 in case of Mukherjee and Brill correlation and R 2 = 0.91 when a combination of Ansari and Xiao mechanistic models is utilized. The application of the above-mentioned ML algorithms and the larger database used for their training will allow extending the proposed methodology to a wider applicability range of input parameters as compared to standard accessible techniques. The method for pressure drop prediction based on ML algorithms trained on lab data is also validated on three real field cases. Validation indicates that the proposed model yields the following coefficients of determination: R 2 = 0.806, 0.815 and 0.99 as compared with the highest values obtained by commonly used techniques: R 2 = 0.82 (Beggs and Brill correlation), R 2 = 0.823 (Mukherjee and Brill correlation) and R 2 = 0.98 (Beggs and Brill correlation). Hence, the method for calculating the pressure distribution could give comparable or even higher scores on field data by contrast to correlations and mechanistic models. This fact is an indicator that the model can be scalable from the lab to the field conditions without any additional retraining of ML algorithms.

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“…Limitations of linear regression model applied for water cut prediction were discussed. Recent study [40] used gradient boosting to solve the regression problem for predicting the production rate after the simulation treatment on a data set of 270 wells. Mathematical model was formulated in detail, though data sources and the details of data gathering and preprocessing were not discussed.…”

Section: For Frac Design Optimizationmentioning

confidence: 99%

Machine learning on field data for hydraulic fracturing design optimization

Mutalova¹,

Morozov²,

Осипцов³

et al. 2019

Preprint

Self Cite

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Growing amount of fracturing stimulation jobs in the recent two decades resulted in a significant amount of measured data available for construction of predictive models via machine learning (ML). Simultaneous evolution of machine learning has made it possible to apply algorithms on the hydraulic fracture database. A typical multistage fracturing job on a near-horizontal well today involves a significant number of stages. The post-fracturing production analysis (e.g., from production logging tools) reveals evidence that different stages produce very non-uniformly, and up to 30% may not be producing at all due to a combination of geomechanics and fracturing design factors. Hence, there is a significant room for fracturing design optimization, and the wealthy of field data combined with ML techniques opens a new road for solving this optimization problem. However, ML algorithms are only applicable when there is a comprehensive, well structured digital database. This paper summarizes the efforts into the creation of a digital database of field data from several thousands of multistage hydraulic fracturing jobs on near-horizontal wells from several different oilfields in Western Siberia, Russia. In terms of the number of points (fracturing jobs), the present database is a rare case of an outstandingly representative dataset of thousands of cases, compared to typical databases available in the literature, comprising tens or hundreds of pints at best. The focus is made on data gathering from various sources, data preprocessing and development of the architecture of a database as well as solving fracture design optimization via ML. We work with the database composed from reservoir properties, fracturing designs and production data. Both forward problem (prediction of production rate from fracturing design parameters) and inverse problem (selecting an optimum set of frac design parameters to maximize production) are considered. A recommendation system is designed for advising a DESC engineer on an optimized fracturing design.

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Gradient boosting to boost the efficiency of hydraulic fracturing

Cited by 31 publications

References 23 publications

Data-driven approaches tests on a laboratory drilling system

Data-driven approaches tests on a laboratory drilling system

A predictive model for steady-state multiphase pipe flow: Machine learning on lab data

Machine learning on field data for hydraulic fracturing design optimization

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