Submit to Energies Review for Energies Propose a Special Issue

Journal Browser

► Journal Browser

Hydraulic Fracturing: Progress and Challenges

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "H1: Petroleum Engineering".

Deadline for manuscript submissions: closed (20 October 2022) | Viewed by 14667

Share This Special Issue

Special Issue Editors

Prof. Dr. Vamegh Rasouli

E-Mail Website
Leading Guest Editor

Department of Petroleum Engineering, College of Engineering & Mines, University of North Dakota, Grand Forks, ND 58202, USA
Interests: petroleum geomechanics; wellbore stability; hydraulic fracturing; sanding and sand prevention; fractured reservoir characterization; geomechanics of unconventional reservoirs; hard rock drilling

Dr. Fred Aminzadeh

E-Mail Website
Guest Editor

FACT Inc., 3345 State Street, Suite 3282, Santa Barbara, CA 93130, USA
Interests: geophysics; artificial intelligence; hydraulic fracturing; reservoir monitoring; CO₂ sequestration

Dr. Uchenna Odi

E-Mail Website
Assistant Guest Editor

Aramco Americas, Upstream, 1200 Smith Street, Room 35.016, Houston, TX 77002, USA
Interests: enhanced oil recovery; reservoir engineering; chemical engineering; petroleum engineering; machine learning

Dr. Ali Rezaei

E-Mail Website
Assistant Guest Editor

Schlumberger, 1325 S. Dairy Ashford, Houston, TX 77077, USA
Interests: hydraulic fracturing; reduced-order modeling; machine learning

Special Issue Information

Dear Colleagues,

The Journal of Energies mission is to publish peer-reviewed, original research seeking sustainable methods of worldwide energy production through engineering, scientific, and technological advances. Hydraulic fracturing’s transformative impact on the exploitation of shale rocks has been one of the most significant developments in the energy-related topic in the oil & gas industry. Aside from the evolution of hydraulic fracturing technology, its economic impact and environmental considerations will be the focus of this special issue. Of particular interest to the scientific community is to improve our understanding of the production patterns observed from long multi-stage lateral completions despite the ultra-low permeability of these reservoirs. Furthermore, our knowledge of these unconventional reservoirs’ physical and mechanical behavior under varying stress conditions is highly limited at best. While “fracture complexity” has been routinely used to explain the vast discrepancies between actual observations during fracture treatments and the predictive models, more research is needed to better understand these differences and optimize future completions. In terms of diagnostic tools, incorrect use of techniques such as microseismic mapping by oversimplifying or ignoring limitations has led to erroneous predictions. Thus, there is a need to continue working on such techniques to improve their applicability, provide additional tools for data analysis, and quantify issues such as uncertainty. Apart from these focused research topics, the broader scientific community would benefit from past experiences (case histories), environmental studies, and the economic impacts of hydraulic fracturing. We will be aiming to build upon the remarkable success that hydraulic fracturing has had in the past two decades to improve our understanding of this process by plugging various technology gaps that are observed today. With the advances that artificial intelligence (AI) and data analytics (DA) have made in different aspects of oil and gas exploration and production, we will also seek to investigate how AI-DA could be employed to further advance the effectiveness of hydraulic fracturing. Papers are invited on 1) Hydraulic fracturing related topics in general, 2) Recfracturing, 3) Infill well fracturing, 4) Production patterns, 5) Geomechanics and Stress computations, 6) Proppant placement, 7) Microseismic mapping and seismic hazards, 8) Novel Diagnostics Methodologies, 9) Artificial Intelligence and Data Analytics, and 10) Economics, Safety, and Environmental.

Prof. Dr. Vamegh Rasouli
Dr. Fred Aminzadeh
Dr. Uchenna Odi
Dr. Ali Rezaei
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Hydraulic fracturing
Recfracturing
Infill well fracturing
Production patterns
Geomechanics and Stress computations
Proppant placement
Microseismic mapping and seismic hazards
Novel Diagnostics Methodologies
Artificial Intelligence and Data Analytics
Stimulated Reservoir Volume (SRV)
Economics, Safety, and Environmental

Published Papers (8 papers)

Download All Papers

Research

15 pages, 99160 KiB

Open AccessArticle

Using Machine Learning to Predict Multiphase Flow through Complex Fractures

by Allen K. Ting, Javier E. Santos and Eric Guiltinan

Energies 2022, 15(23), 8871; https://doi.org/10.3390/en15238871 - 24 Nov 2022

Cited by 3 | Viewed by 1715

Abstract

Multiphase flow properties of fractures are important in engineering applications such as hydraulic fracturing, evaluating the sealing capacity of caprocks, and the productivity of hydrocarbon-bearing tight rocks. Due to the computational requirements of high fidelity simulations, investigations of flow and transport through fractures typically rely on simplified assumptions applied to large fracture networks. These simplifications ignore the effect of pore-scale capillary phenomena and 3D realistic fracture morphology (for instance, tortuosity, contact points, and crevasses) that lead to macro-scale effective transport properties. The effect of these properties can be studied through lattice Boltzmann simulations, but they require high performance computing clusters and are generally limited in their domain size. In this work, we develop a technique to represent 3D fracture geometries and fluid distributions in 2D without losing any information. Using this innovative approach, we present a specialized machine learning model which only requires a few simulations for training but still accurately predicts fluid flow through 3D fractures. We demonstrate our technique using simulations of a water filled fracture being displaced by supercritical CO

_{2}

. By generating highly efficient simulations of micro-scale multiphase flow in fractures, we hope to investigate a wide range of fracture types and generalize our method to be incorporated into larger discrete fracture network simulations. Full article

(This article belongs to the Special Issue Hydraulic Fracturing: Progress and Challenges)

► Show Figures

Figure 1

14 pages, 3615 KiB

Open AccessArticle

On the Stability of Particle–Particle Interaction during Gravitational Settling

by Mazen Hafez, Mahyar Ghazvini and Myeongsub Kim

Energies 2022, 15(22), 8721; https://doi.org/10.3390/en15228721 - 20 Nov 2022

Cited by 4 | Viewed by 1018

Abstract

The elevated energy demand and high dependency on fossil fuels have directed researchers’ attention to promoting and advancing hydraulic fracturing (HF) operations for a sustainable energy future. Even though previous studies have demonstrated that the proppant suspension and positioning in slickwater play a vital role during the shut-in stage of the HF operations, minimal experimental work has been conducted on the fundamental proppant–proppant interaction mechanisms, especially a complete mapping of the interactions. This study utilizes high-speed imaging to provide a 2D space- and time-resolved investigation of two-particle (proppant models: 2 mm Ø, 2.6

g \cdot {cm}^{- 3}

) interactions during gravitational settling in different initial spatial configurations and rheological properties. The mapping facilitates the identification of various interaction regimes and newly observed particle trajectories. Pure water results at a settling particle Reynolds number

(R e_{p})

~ 470 show an unstable particle–particle interaction regime characterized by randomness while altering pure water to a 25% (v/v) water–glycerin mixture

(R e_{p} ~ 200)

transitions an unstable interaction to a stable prominent repulsion regime where particles’ final separation distance can extend up to four times the initial distance. This indicates the existence of

R e_{p}

at which the stability of the interactions is achieved. The quantified trajectories indicate that when particles are within minimal proximity, a direct relation between repulsion and

R e_{p}

exists with varying repulsion characteristics. This was determined by observing unique bottle-shaped trajectories in the prominent repulsion regimes and further highlighted by investigating the rate of lateral separation distance and velocity characteristics. Additionally, a threshold distance in which the particles do not interact (or negligibly interact) and settle independently seems to exist at the normalized 2D lateral separation distance. Full article

(This article belongs to the Special Issue Hydraulic Fracturing: Progress and Challenges)

► Show Figures

Figure 1

15 pages, 7607 KiB

Open AccessArticle

Classifying Facies in 3D Digital Rock Images Using Supervised and Unsupervised Approaches

by Cenk Temizel, Uchenna Odi, Karthik Balaji, Hakki Aydin and Javier E. Santos

Energies 2022, 15(20), 7660; https://doi.org/10.3390/en15207660 - 17 Oct 2022

Cited by 4 | Viewed by 1306

Abstract

Lithology is one of the critical parameters influencing drilling operations and reservoir production behavior. Well completion is another important area where facies type has a crucial influence on fracture propagation. Geological formations are highly heterogeneous systems that require extensive evaluation with sophisticated approaches. Classification of facies is a critical approach to characterizing different depositional systems. Image classification is implemented as a quick and easy method to detect different facies groups. Artificial intelligence (AI) algorithms are efficiently used to categorize geological formations in a large dataset. This study involves the classification of different facies with various supervised and unsupervised learning algorithms. The dataset for training and testing was retrieved from a digital rock database published in the data brief. The study showed that supervised algorithms provided more accurate results than unsupervised algorithms. In this study, the extreme gradient boosted tree regressor was found to be the best algorithm for facies classification for the synthetic digital rocks. Full article

(This article belongs to the Special Issue Hydraulic Fracturing: Progress and Challenges)

► Show Figures

Figure 1

23 pages, 6238 KiB

Open AccessArticle

A Data-Driven Reduced-Order Model for Estimating the Stimulated Reservoir Volume (SRV)

by Ali Rezaei and Fred Aminzadeh

Energies 2022, 15(15), 5582; https://doi.org/10.3390/en15155582 - 1 Aug 2022

Cited by 1 | Viewed by 1278

Abstract

The main goal of hydraulic fracturing stimulation in unconventional and tight reservoirs is to maximize hydrocarbon production by creating an efficient stimulated reservoir volume (SRV) around the horizontal wells. To zreach this goal, a physics-based model is typically used to design and optimize the hydraulic fracturing process before executing the job. However, two critical issues make this approach insufficient for achieving the mentioned goal. First, the physics-based models are based on several simplified assumptions and do not correctly represent the physics of unconventional reservoirs; hence, they often fail to match the observed SRVs in the field. Second, the success of the executed stimulation job is evaluated after it is completed in the field, leaving no room to modify some parameters such as proppant concentration in the middle of the job. To this end, this paper proposes data-driven and global sensitivity approaches to address these two issues. It introduces a novel workflow for estimating SRV in near real-time using some hydraulic fracturing parameters that can be inferred before or during the stimulation process. It also utilizes a robust global sensitivity framework known as the Sobol Method to rank the input parameters and create a reduced-order (mathematically simple) model for near real-time estimation of SRV (referred to as DSRV). The proposed framework in this paper has two main advantages and novelties. First, it is based on a pure data-based approach, with no simplified assumptions due to the use of a simulator for generating the training and test dataset, which is often the case in similar studies. Second, it treats SRV generation as a rock mechanics problem (rather than a reservoir engineering problem with fixed fracture lengths), accounting for changes in hydraulic fracture topology and SRV changes with time. A dataset from the Marcellus Shale Energy and Environment Laboratory (MSEEL) project is used. The model’s input parameters include stimulation variables of 58 stages of two wells. These parameters are stage number, step, pump rate and duration, proppant concentration and mass, and treating pressure. The model output consists of the corresponding microseismic (MS) cloud size at each step (i.e., time window) during the job. Based on the model, guidelines are provided to help operators design more efficient fracturing jobs for maximum recovery and to monitor the effectiveness of the hydraulic fracturing process. A few future improvements to this approach are also provided. Full article

(This article belongs to the Special Issue Hydraulic Fracturing: Progress and Challenges)

► Show Figures

Figure 1

17 pages, 2638 KiB

Open AccessArticle

Modeling Temporal Dependence of Average Surface Treating Pressure in the Williston Basin Using Dynamic Multivariate Regression

by Josh Kroschel, Minou Rabiei and Vamegh Rasouli

Energies 2022, 15(6), 2271; https://doi.org/10.3390/en15062271 - 21 Mar 2022

Cited by 1 | Viewed by 1519

Abstract

The oil and gas industry has shifted paradigms after seeing the drastic decrease in oil prices since 2015. Companies are now focused as much on cost reduction as much as production maximization to drive profitable operations. This aspect is more prevalent in unconventional plays with the need for long horizontal drilling and hydraulic fracturing (HF) operations to develop and produce from the tight reservoirs. There exists an optimum point between the costs of HF treatment and the expected production. Because of the paradigm shift, many operators are now focused on re-developing existing assets at much lower costs instead of developing newer, more costly assets. Re-fracturing existing wells provides an opportunity for companies to add economical wells to their portfolio. Re-fracturing consists of pumping HF treatments in wells that were previously drilled and completed. Although it may seem that the HF process on a well would be easier the second time around, this is not always the case. There are often numerous operational and engineering parameters that may cause screen outs due to excessively high surface treating pressure (STP) that can drastically affect the economics of a re-fractured well. Being able to isolate the effects of these parameters and estimate their marginal effect on treatment will help engineers design to better HF treatments and surface equipment to effectively implement treatments in the field. This novel study uses field treatment data from re-fractured wells to create dynamic multivariate regression models to characterize the effects of treatment parameters on the average STP. The model allows for engineers to isolate the effects of other treatment parameters and estimate their marginal effects on average STP by holding other variables of interest constant. The model also attempts to account for the temporal dependence of stress shadow effects from the previous zones by using the average STP as a good approximation. It was found that the distance between zones (perforation standoff) was statistically significant at the 90% level, average pump rate, acid volume displaced, and the presence of a 3.5” liner were all statistically significant predictors of average STP at the 95% level and average surface treating pressure from the previous stage at 99% significance. The model was used to predict the STP for another re-fractured well, which showed reasonable results. Full article

(This article belongs to the Special Issue Hydraulic Fracturing: Progress and Challenges)

► Show Figures

Figure 1

35 pages, 12781 KiB

Open AccessArticle

Mathematical Modeling of Hydraulic Fracture Formation and Cleaning Processes

by Nickolay Smirnov, Kairui Li, Evgeniya Skryleva, Dmitriy Pestov, Anastasia Shamina, Chengzhi Qi and Alexey Kiselev

Energies 2022, 15(6), 1967; https://doi.org/10.3390/en15061967 - 8 Mar 2022

Cited by 5 | Viewed by 1895

Abstract

The effectiveness of the hydraulic fracturing procedure is crucially dependent on the stage of fracture planning and design. Forecasting fracture behavior in rock formations characterized by non-uniform toughness is a serious challenge. In the present paper, a planar-3D model considering the rock’s non-uniform fracture toughness has been developed for the uneven propagation of a hydraulic fracture. The series of numerical experiments were designed to study the effect of inhomogenous fracture toughness. The results show that the fracture toughness contract significantly controls the overall direction of fracture propagation, and a combination of toughness contrast and the proportion between the pay zone and barrier zone determine the fracture profile: from almost circular with or without a pair of narrow wedges when the proportion is small to almost rectangular otherwise. This paper also discusses the process of cleaning a fracture from hydraulic fracturing fluid by oil. Using numerical modeling on the basis of the constructed mathematical model, a relationship is established between the quality of hydraulic fracture cleaning and the geometrical parameters of the fracture and the region filled with the hydraulic fracturing fluid. The results of numerical experiments show that while fracturing fluid is more viscous than oil, the length of the fracture has a greater influence on the cleaning process than the viscosity of the fracturing fluid. Full article

(This article belongs to the Special Issue Hydraulic Fracturing: Progress and Challenges)

► Show Figures

Figure 1

24 pages, 7863 KiB

Open AccessArticle

Production Improvement via Optimization of Hydraulic Acid Fracturing Design Parameters in a Tight Carbonate Reservoir

by Rahman Ashena, Fred Aminzadeh and Amir Khoramchehr

Energies 2022, 15(5), 1947; https://doi.org/10.3390/en15051947 - 7 Mar 2022

Cited by 6 | Viewed by 2494

Abstract

Hydraulic fracturing can be utilized to extract trapped hydrocarbon where integrated fracture networks do not exist for sufficient production. In this work, design parameters of a hydraulic acid fracturing of a tight carbonate reservoir in the Middle East were optimized. The effect of optimized hydraulic fracturing on production performance and rate was investigated. Using the petrophysical well logs, formation integrity tests, core data the Mechanical Earth Model (MEM) of the tight carbonate reservoir was created, which resulted in rock mechanical properties and in-situ stresses. The other required parameters for fracturing design were either measured or found from empirical correlations. Following a candidate selection of suitable layers for fracturing, the input parameters were loaded in GOHFER software to design and optimize the fracturing job. Finally, the production forecast was performed and compared with current conditions. The injection parameters (flow rate, total volume, and number of stages) of the fracturing fluid (composed of guar and CMHPG and polymer with 15% HCL acid) were optimized to reach optimum resultant fracture geometry. Finally, optimized injection parameters were found at the injection flow rate of 18 barrels per minute, total injection volume of 90 K-gal, and three stages of injection. Using the optimal injection parameters, the optimized fracture geometrical sizes were determined: the fracture half-length (L_f): 148 m (486 ft), fracture height (H_f) of 64 m (210 ft) and fracture width (W_f) of 0.0962 in. Finally, the effect of this stimulation method on future production performance was investigated. The well production rate showed an increase from 840 STB/Day (before fracturing) to 1270 STB/Day (post fracturing). This study contributes to the practical design and optimization of hydraulic fracturing in the tight carbonate formation of the investigated oilfield and the other potential fields in the region. The results showed that this stimulation method can efficiently improve production performance from reservoir formation. Full article

(This article belongs to the Special Issue Hydraulic Fracturing: Progress and Challenges)

► Show Figures

Figure 1

15 pages, 5773 KiB

Open AccessArticle

Damage Model for Reservoirs with Multisets of Natural Fractures and Its Application in the Simulation of Hydraulic Fracturing

by Huifang Hu, Tian Shen, Naiyuan Zheng, Xinpu Shen and Jinbiao Yu

Energies 2022, 15(4), 1462; https://doi.org/10.3390/en15041462 - 17 Feb 2022

Viewed by 1283

Abstract

The presence of natural fractures can significantly affect the quality of hydraulic fracturing operations in tightsand and shale or oil/gas formations. This paper describes the procedure used to model natural fractures with continuum damage tensor and the resulting orthotropic permeability tensor. A damage model that uses damage variable in tensor form is presented. In the procedure presented, a nonnegligible angle is assumed to exist between directions of principal stresses in the formation and in the natural-fractures-related damage tensor, and this difference in orientation is modeled by introducing local directions in the model. A damage-dependent permeability tensor in tabular form is then proposed. A second case scenario when the directions of principal stresses and natural fractures align is also analyzed. Numerical results of fracture distribution are presented for both cases, and differences can be seen from the computed contour of the damage variable. The results indicate that the model can effectively simulate the fracture propagation phenomena during hydraulic fracturing. Full article

(This article belongs to the Special Issue Hydraulic Fracturing: Progress and Challenges)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Hydraulic Fracturing: Progress and Challenges

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (8 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI