Alkinani, Husam H. (Missouri University of Science and Technology) | Al-Hameedi, Abo Taleb T. (Missouri University of Science and Technology) | Dunn-Norman, Shari (Missouri University of Science and Technology) | Al-Alwani, Mustafa A. (Missouri University of Science and Technology) | Lian, David (Missouri University of Science and Technology) | Al-Bazzaz, Waleed H. (Kuwait Institute For Scientific Research)
It is not easy to obtain an optimal hole cleaning for the drilling operation because of the complicated relationship between the drilling parameters influencing hole cleaning. The two viscosity components (e.g. plastic viscosity (PV) and yield point (YP)) and the flow rate (Q) are essential parameters for effective hole cleaning. Thus, understanding the relationship between those parameters will contribute to efficient hole cleaning. The aim of this paper is to explore those relationships to provide optimal hole cleaning.
Descriptive data analytics was conducted for data of more than 2000 wells drilled in Southern Iraq. The data were first cleansed and outliers were removed using visual inspection and box plots. The Pearson correlation (PC), a widely used method to measure the linear relationship between two parameters, was utilized to access the relationships between PV and Q, YP and Q, and YP/PV and Q. Moreover, a 10% sensitivity analysis was escorted to quantify and comprehend those relationships.
The PCs were calculated to be 0.5, 0.076, and 0.22 for the relationships between YP, PV, and YP/PV with Q, respectively. YP had the highest direct relationship with Q, while PV had the lowest. When the YP increases, a sufficient Q has to be provided to initiate the flow and maintain the mud cycle. In addition, to prevent large solid particles from settling due to the slip velocity, sufficient annular and particle velocities have to be achieved. After initiating the flow, an increase in flow rate to overcome resistance due to PV will not be significant. Therefore, YP has more effect on Q than PV. To maximize hole cleaning, thickening ratio (YP/PV) should be increased. This requires an increase in flow rate, which can be quantified by using the sensitivity analysis provided to achieve the required Q for any increase in YP/PV.
Al-Hameedi, Abo Taleb T. (Missouri University of Science and Technology) | Alkinani, Husam H. (Missouri University of Science and Technology) | Dunn-Norman, Shari (Missouri University of Science and Technology) | Al-Alwani, Mustafa A. (Missouri University of Science and Technology) | Lian, David (Missouri University of Science and Technology)
Flow rate (Q) affects many drilling operations and parameters such as equivalent circulation density (ECD), hoisting and lowering the drillstring, and breaking gel strength during circulation. The aim of this work is to understand the relationship between ECD and Q based on flow regimes (e.g. laminar, transitional, and turbulent) to avoid or at least minimize the unwanted consequence during drilling practice.
Field data from over 2000 wells drilled in Iraq were collected and analyzed to identify the physical relationship between flow regimes and ECD to enhance the drilling rates. After visualizing the whole dataset, a decision was made to break down the data into three parts based on flow regimes (e.g. laminar, transitional, and turbulent). Descriptive data mining techniques were utilized to establish the relationship between flow regimes and ECD. By achieving better control of ECD in the well, not only faster and cheaper operations are possible, but also safety will be improved.
Previous studies and literature showed that flow regimes can tremendously affect ECD. Many studies have been conducted to understand the relationship between Q and ECD. Nevertheless, the consideration of flow regimes was not implemented in these studies. Inconsistency in the literature results was identified, some concluded the relationship between Q and ECD to be direct, and others showed it to be inverse. Thus, this paper will eliminate this discrepancy in the literature, and it will show that the flow regimes have a pivotal role in the relationship between Q and ECD.
The results of this paper showed that if the flow regime is laminar, the relationship between ECD and Q is inverse. However, in transitional and turbulent flow regimes, the relationship between ECD and Q is direct. That is because, in the laminar flow regime, the cutting will fall out of suspension due to low Q, which will cause a cutting bed to be built and decreases ECD. As Q increases (entering the transitional and turbulent flows) the cutting bed will be eroded, and most of the cuttings will be suspended in the fluid which will increase ECD.
This study examines and expands the understanding between how the characteristics of flow regimes affect ECD. Additionally, this paper will eliminate the discrepancy in the literature about this relationship between ECD and Q.
Alkandari, Dana K. (Australian College of Kuwait) | AlTheferi, Ghaneima M. (Australian College of Kuwait) | Almutawaa, Hawra'a M. (Australian College of Kuwait) | Almutairi, Maryam (Australian College of Kuwait) | Alhindi, Nora (Australian College of Kuwait) | Al-Rashid, Sherifa M. (Australian College of Kuwait) | Al-Bazzaz, Waleed H. A. (Kuwait Institute For Scientific Research)
Formation damage is the impairment of permeability of rocks inside a petroleum reservoir. This occurs during drilling, production, stimulation and enhanced oil recovery operations, by various mechanisms such as chemical, mechanical, biological and thermal. Near wellbore formation damages have a great impact on productivity of the damaged formation. Acidizing is a stimulation method to remove the effect of near wellbore damage through reacting with damaging materials or the formation rocks (carbonate or sandstone rocks) to restore or improve permeability around the wellbore. Several experiments are conducted to study the effect of temperature and acid concentration combined on the efficiency of matrix acidizing. Three different concentrations scenarios of hydrochloric acid (3%, 15%, and 28%) and 4 different temperatures scenarios (25 °C, 35 °C, 70 °C, and 100 °C) were tested to investigate pore-enlargement success effect on permeability. The purpose of this experiment is to introduce the concept of optimized temperature augmented with optimized acid concentration in carbonate matrix acidulation. Morphology of pore geometry and area measurement software is used. New Advancement in imaging that captured pore area enlargement as big-data necessarily for artificial intelligence modeling. Captured pores before treatment and captured pores after thermal-HCL acid treatment have demonstrated that image processing of the actual acidized rock data can select the optimized recipe concentration of acid that will increase permeability, hence recovery. The results show that matrix acidizing is an effective method to improve permeability and enhance production, as it demonstrates that using less acid concentration with the optimized temperature can result in a favorable and satisfying outcomes.
Descriptive Analytics is the first step of a three-step data-driven analytics workflow used for managing and optimizing completion, production and recovery of shale wells. The comprehensive data-driven analytics workflow for the unconventional resources is called Shale Analytics (
Shale Descriptive Analytics takes into account seven categories of field measurements;
Two conclusions have been achieved as the result of this study.
Eustes III, Alfred W. (Colorado School of Mines) | McKenna, Kirtland I. (Colorado School of Mines) | Zody, Zach J. (Colorado School of Mines) | Healy, Carl (Colorado School of Mines) | Lang, Camden (XTO) | Joshi, Deep (Colorado School of Mines) | Yow, Stephen (Chevron) | McGowen, Kyle (Shell)
Drilling education must evolve continuously to keep up with the changes in the drilling industry. Part of that evolution includes the addition of data analytics in drilling operations. In addition, having a "hands on" experience of actual drilling operations is an important part of the drilling engineering educational process. At the Colorado School of Mines, both goals are achieved using a new coring rig equipped with a high-frequency data acquisition system.
A Sandvik DE 130 Diamond Coring Rig was acquired by the school through a grant from Apache Corporation that has proven to be an excellent analog to full-scale petroleum rigs. It has all drilling subsystems such as rotary, hoisting, power, and circulation. A data acquisition system has been added that tracks accelerations as well as various drilling operational parameters. During experiments, each student has an opportunity to operate the driller's controls and experience the complexities associated with drilling operations including the occasional error. The retrieved core helps the student correlate the formation with drilling data.
The inclusion of the drilling experience in the curriculum has benefited the students in several aspects. This experience has helped students visualize drilling operations and understand complexities and challenges associated with drilling. During the drilling operations, if any problems arise, the students have a chance to troubleshoot those problems in real-time and apply their theoretical knowledge. Operational safety and stop work authority are also a focus and demonstrated by students. This is likely to be the first experience most students have with high-frequency drilling data analysis. Monitoring, collecting, and handling real high-volume data gives a first glimpse into the complexities of data analytics. Noisy realtime data and errors are real and observed by the students. They also learn to handle and analyze high- frequency drilling data identifying normal trends and abnormalities. This coring rig has enhanced the drilling engineering education and data analysis skills of our students.
This work outlines a novel teaching methodology that combines the practical understanding of drilling and the application of data analytics. Getting out to the field and actually drilling rock has enhanced our drilling curriculum to align it with the latest industry practice and to educate future drilling engineers.
The objective of this project, named DataGMA after Data Governance, Data Management and Data Analytics, is to transform YPF shale operations to a data-driven culture. Recognizing that Data Governance, Management & Analytics (DGM&A) problem solving approach requires a wide interaction between the concerned players is the first step to delineate a sustainable and scalable outcome regarding DGM&A best practices. Increasing data-awareness leads to an active engagement of the whole organization around the data life cycle which, in turn, is the basis of a DGM&A virtuous cycle. This paper presents an integrated approach through a multidisciplinary task force (DGM&A IT Upstream Technical Staff) designed not only to tackle the data challenges YPF faces but, and more importantly, to build data awareness and engagement around the data life cycle among the Upstream Technical Team. This combined effort assists the organization in understanding the broader picture of the situation through an exhaustive assessment and an accurate diagnosis.
During the last few years, the petroleum industry has been experiencing significant changes in various areas including, workforce, targets of exploration, application of (new) technologies, and general operational areas of focus. A prolonged depression of oil prices, changes in geopolitical atmosphere, the rise of investment in unconventional resources, as well as the implementation of emerging technologies (including digital) have been the primary catalysts of change within the industry. In terms of workforce, these changes have produced leaner organizations, along with the unintended consequence of losing some critical expertise and creating knowledge-gaps at many organizations. The changes, particularly in technology, necessitate a look at the need for the acquisition of new skills, for current and future petroleum engineers, that match new areas of interest – such as data analytics and artificial intelligence.
As the oil industry continues to evolve, it is imperative for academic organizations to consider these changing dynamics and be responsive. This paper outlines the results of a recent survey that targeted industry managers or supervisors who have direct experience with newly minted petroleum engineering graduates (less than five years of experience). The survey asked the participants their opinions regarding the preparedness of recent graduates as they enter the workforce. The survey's intent was to identify the potential need to modify the skills and knowledge currently acquired in academic institutions during the undergraduate study.
A comprehensive survey that posed questions regarding classical and contemplated new petroleum engineering curriculum was sent to recipients, primarily within the reserves and reservoir-engineering sector. The recipients were industry professionals working in operating, service, financial, and consulting sectors of the petroleum industry. More than 200 responses were received. The tabulated results are presented in the paper, along with interpretation of the results. The raw data will be made available through OnePetro as an accompaniment to the published paper.
The paper presents the survey conclusions, proposed action items, and discusses plans for a follow-up survey.
Within a single field geophysical survey results always have a significant amount of data with a considerable variability and heterogeneity. This allows to classify geophysical data as a Big Data. Data scientists and software developers are increasingly recommending the use of machine learning techniques for data processing and interpretation. ML algorithms allow one to extract the most complete amount of useful information, reduce time costs, minimize the subjective factor in the decision-making process, etc. Early testing of these approaches began in the 60s, active practical implementation consisted in the 90s due to the large-scale implementation of seismic studies in 3D CDP modification 1. The emergence of new algorithms, modifications of the original data, the development of computational resources support the relevance of this topic at the present time. In seismic data interpretation machine learning approaches provide high performance in the process of automatic horizons picking, fault tracing, seismic facies analysis, sesimic inversion, reservoir prediction, etc. At the stage of seismic facies analysis application of the ML algorythms is especially effective since in the process of multiattribute classification the initial dataset increases severalfold in accordance with the number of calculated attributes 5-7, 9, 10.
Cao, Richard (Shell Exploration and Production Co.) | Chen, Chaohui (Shell Exploration and Production Co.) | Girardi, Alejandro (Shell Exploration and Production Co.) | Li, Ruijian (Shell Exploration and Production Co.) | Chowdhury, Nitin (Shell Exploration and Production Co.)
Optimum co-development layout of multiple targets for unconventional reservoirs is extremely challenging due to complex 3-dimentional well interactions, stochastic well performance, complex fracture geometry, dynamic SRV/DRV evolution, heterogeneous rock properties, various operating conditions, and different economic drivers. In this study, an integrated workflow is developed and applied for co-development of multiple targets in Permian unconventional reservoirs. In this workflow, the field pilot and trial measurements, Microseismic, geochemistry measurement, data analytics, detailed geomechanical and reservoir modeling, stochastic multiple history matching and forecast, all combined to quantify the horizontal and vertical interference factors and obtain production profiles for different co-development designs. The stochastic behavior of the well performance is explored from three different aspects: static rock properties, dynamic fracturing, and production. The SRV/DRV evolution are presented as the probability distribution function of half fracture length from Microseismic data and effective drainage half-length from stochastic modeling.
Current multistage hydraulic fracturing operations in shale are costly, environmentally challenging and inefficient. Multistage hydraulic fracturing operations already represent close to 60% of the total drilling and completion cost for each shale well. The industry studies reported that based on data evaluated in multiple shale basins in North America alone that up to 50% of the clusters and stages do not produce in geometric completion design. Shale E&P operators need more accurate, cost-efficient, timely and actionable data on the performance of individual fracturing stages and intra-well communication to enable improved decision-making and optimization of multistage hydraulic fracturing and completion strategy, as well as overall field development.
This paper will describe a revolutionary smart tracer portfolio testing and design for multistage hydraulic fracturing stimulation. The technology enables the next generation of smart tracers coupled with advanced sub-atomic measurements that significantly reduce the completion cost and double the efficiency of the hydraulic fracturing treatments. An automated process with stringent quality control assured precise tracer addition onsite and provided accurate and actionable completion diagnostics results at fraction of the cost for high-cost measurements (e.g., PLT, DTS & DAS).
The integration of smart tracer portfolio with intelligent-completion diagnostics for E&P customer enabled by performance-flow-profile data. This data used to optimize completion strategies, achieve optimal production per foot, and reduce completion cost. Follow-up big-data analytics and 3D fracture-modeling delivered accurate, calibrated, actionable, and cost-effective completion-diagnostics results. Since tracer data are captured over several months, E&P operators are captured access to continuous flow profiling data to optimize well performance routinely when new completion-diagnostics results are received. This will enable E&P operators to significantly reduce operating cost and optimize production in shale wells.