Mahshad Samnejad | University of Southern California (original) (raw)

Papers by Mahshad Samnejad

1. BACKGROUND United States has benefited economically from the boom in horizontal drilling and h... more 1. BACKGROUND United States has benefited economically from the boom in horizontal drilling and hydraulic fracturing technology, which rendered the development of domestic unconventional energy resources possible. US production of liquid fuels surpassed the Middle East in 2013 [Yo and Neff, 2014], adding 169,000 jobs between 2010 and 2012 [Brown and Yucel, 2013]. Reducing a country's dependence on the imported energy helps mitigate economic losses caused by foreign oil supply disruptions [Brown and Huntington, 2009]. The success of investment decisions pertaining to the exploitation of unconventional resources depends strongly on the reliability of models making predictions of post-stimulation performance. However, due to a lack of models based on accurate knowledge of the reservoir and rigorous understanding of the governing physics, there is a technology gap between the current models of stimulation and the field observations in the E&P industry. A major drawback associated with common hydraulic fracturing simulation methods is that they need prior knowledge on the fracturing path, meaning the outcome of the stimulation job should be fed as input to the model, rather than obtained as output. In addition, prevalent approaches for modeling performance of hydraulic fracturing jobs often fail to quantify the job results realistically, as linear elasticity and rock brittleness are the main underlying assumptions of most models. It has been shown, however, that there are a number of influence factors that need to be accounted for in prediction models. Brittle materials demonstrate a shorter period of ductile deformation before failure, which does not necessarily guarantee easier fracturing at lower ultimate rock strength values. Bai, 2016 states that, in fact, certain ductile formations may break at lower downhole pressures based on field measurements. Papanastasiou, 1997 incorporated the effect of plasticity in hydraulic fracturing using a cohesive crack model, showing that ductile rock behavior can lead to higher resulting fracture width values, while creating fractures with a smaller length. These observations suggest that limiting our target rocks and prediction models to linear elastic materials leads to inaccurate conclusions, since both mechanisms of brittle and ductile fracturing need to be considered for better modeling purposes. ABSTRACT: The success of hydraulic fracturing jobs is often related to rock brittleness indices, which are taken as the sole impact factor determining fracturing results. Indeed, hydraulic fractures play a principal role in producing from low-permeability reservoirs; however, brittleness is not the only parameter contributing to productivity of unconventional resources. Under a variety of circumstances, brittleness indices are insufficient to explain rock fracability and permeability enhancement during hydraulic stimulation. For better prediction and design, it is imperative to identify and understand other factors affecting fracture creation and propagation, and to build models that include the effect of these factors on flow enhancement. To numerically model permeability enhancement after injection, we can regard fractured rock as a damaged continuum, which allows simulation of the deformation and fracturing response of the reservoir using material constitutive laws for brittle and ductile regions. We outline a coupled flow-geomechanical simulation framework that fits into available reservoir simulation platforms and does not require pre-specified fracture paths. We develop the fracture growth mechanisms for the coupled simulation framework by analyzing the effect of rock properties and in-situ stresses on the fracture length at different injection pressures. Based on these mechanisms, we propose factors that quantify the success of hydraulic fracturing jobs beyond the simplified rock brittleness indices.

SPE Annual Technical Conference and Exhibition, 2017

SPE Western Regional Meeting, 2017

First and foremost, I thank God, the creator and sustainer of the Universe, who has helped me thr... more First and foremost, I thank God, the creator and sustainer of the Universe, who has helped me throughout my personal life and academic journey. I would like to express my deep appreciation to my Ph.D. advisor, Dr. Fred Aminzadeh, who has motivated and supported me throughout my graduate studies. His broad insight in the oil and gas domain leads to constantly seeking novel solutions to the challenging problems of the petroleum industry in innovative practice-oriented ways. I thank him for his belief in my work, benevolent pieces of advice, and thoughtful encouragements at difficult times during my Ph.D. enrollment. I am intensely grateful to Professor Iraj Ershaghi for his continuous support over the course of my graduate studies at the University of Southern California (USC). I have benefited from his extensive domain knowledge, profound understanding, and invaluable guidelines at every situation in the past years. My gratitude to him goes beyond words. My sincere thanks also goes to Dr. Birendra Jha for his helpful advice as my coadvisor and helpful suggestions, which improved the quality of my research. I also had the privilege to have Dr. Charles Sammis and Dr. Aiichiro Nakano as my committee members, which enabled me to benefit from their constructive comments. I owe gratitude to the Petroleum Engineering program at the University of Southern California, which provided me with various means to nurture my career in petroleum vi engineering and data science. I also thank Baker Hughes for giving me an internship opportunity which enabled me to put into practice what I had learned at school in the two domains. I am thankful to all my peers and former teammates at USC, who contributed to my research with their help and collaboration. My special thanks go to Dr. Atefeh

Offshore Technology Conference

PRCI-REX2021-040, 2021

Asset owners are required by the government to carry out regular inspection surveys to ensure the... more Asset owners are required by the government to carry out regular inspection surveys to ensure the integrity of all pressure-containing equipment. Conventionally, such operations are performed manually by teams of trained inspectors through visual examinations on-site or remotely. We present an integrated framework for automating the entire process of pipeline inspection without any need for human intervention, pipeline function interruption, or equipment destruction by unleashing the power of the state-of-the-art digital technologies, including deep learning, computer vision, and cloud storage and computing.
Decades of survey videos captured by remotely operated vehicles (ROVs) and drones are broken into frames with optical character recognition (OCR)-extracted time/location stamps, which are annotated by our inspectors to precisely delineate the boundaries of the damaged areas and their specific categories. After extracting images from videos, augmenting the image data, the training set is fed into a deep convolutional neural network architecture equipped with instance segmentation layers for object detection. Trained models are then deployed on the cloud acting as intelligent inspectors for future surveys, allowing also to balance the trade-off between the inference accuracy and performance speed, being crucial to real time usage of the software.
After a pipe segment subject to damage is identified to be imposing an integrity risk, it will automatically be raised as a flag into our linked maintenance infrastructure along with the corresponding spatiotemporal information of the event to take the necessary maintenance, repair, or replacement actions.
The proposed endeavor outperforms common industry practices, as it not only reduces the asset operational costs by eliminating the need for the labor-intensive manual diagnostic inspections, but also improves the hazard mitigation plan by providing accurate risk assessments in shorter time spans.

IADC/SPE International Drilling Conference and Exhibition

SPE-187250-MS, 2017

In hydraulic fracturing operations, permeability is enhanced when fractures are created and/or st... more In hydraulic fracturing operations, permeability is enhanced when fractures are created and/or stimulated by injecting a highly pressurized fluid. In addition, mechanical response of the rock changes because of permanent modifications in the structure and properties of the rock after failure. In order for engineers to accurately predict results of hydraulic stimulation projects, mathematically rigorous and numerically efficient models of fluid flow and geomechanical deformation in fractured porous media must be used in computer simulations. Some of the earlier approaches address the problem of fluid flow through fractured media with mathematical models that are either too simplistic or too expensive computationally and are not compatible with the available petroleum reservoir simulation platforms. In this work, a reservoir simulation framework is developed using a sequentially coupled numerical scheme of flow, deformation and poromechanical damage to study variations occurring in the fractured rock properties and state variables as a result of hydraulic stimulation. We numerically simulate injection-induced permeability enhancement and plastic deformation as well as post-stimulation softening behavior of the rock by considering the stimulated rock as a mechanically damaged configuration, the properties of which are modeled using an effective continuum model. We study how the flow and mechanical properties of fractured rock, namely permeability and stiffness, change by virtue of hydraulic fracturing, and we investigate the dynamics of pressure distribution and stress state with time. The sequential nature of the proposed coupling framework lends itself to easy integration with reservoir simulation and prediction tools.

SPE-185644-MS, 2017

Knowledge of the magnitude of in situ stresses is crucial to a broad range of applications, parti... more Knowledge of the magnitude of in situ stresses is crucial to a broad range of applications, particularly drilling, well completion, and hydraulic fracturing design. Stress field characterization is attained by the determination of magnitude and direction of the three principal stresses: the vertical or overburden stress (S v), the minimum horizontal principal stress (S hmin), and the maximum horizontal principal stress (S Hmax). While numerous direct borehole measurements, such as density logs and leak-off tests, provide reliable assessments of S v and S hmin , estimating local S Hmax remains a challenge. This paper presents a workflow that provides with an approach for estimation and constraining of the maximum horizontal principal stress in a carbonate reservoir from borehole sonic logs, by comparison to the isotropic stress state. Our study was restricted to cases with limited achievable data, namely, conventional logs, which is expected to be the circumstances for most practical applications. The results were compared to published regional stress reports, with the goal of validating and understanding the variations of stresses at the field scale. Those findings were in agreement with the estimations obtained from other acoustoelastic methods and the region fault regime in terms of the relative obtained magnitudes of the stresses. In addition to an increasing trend in the calculated stress magnitudes with depth, we observe local variations of stresses in this oilfield, that we suspected to be caused partially by the non-uniform distribution of production and injection activities. The novelty of our proposed workflow is the ability to give borehole estimates of the maximum horizontal principal stress magnitude for a carbonate field located in a regionally compressive zone, without the need for availability of costly measurements, and by taking advantage of techniques to fill in the gaps of limited data, thus, allowing for integrative use of multiple data types that are commonly available.

ARMA 18–0187, 2018