Joshua Wiley is an incoming engineering student at Texas A&M University. Joshua was raised in the city of Allen, Texas before moving to Magnolia, Texas at 11 years old. Joshua chose to attend an Early College High School to challenge himself and for the opportunity to graduate with an associate degree and a high school diploma within four years. During his time at Tomball Star Academy, Joshua participated in a variety of organizations. Joshua was a Student Council Member, participated in his school's Quiz Bowl team, and joined Phi Theta Kappa. In addition to organizations, Joshua volunteered in his community at the local pet shelter and with the Special Education program at a local junior high school.

Abstract As major oil and gas companies have been investing in renewable energy, renewable energy has been part of the oil and gas industry in the last decade. Originally, renewables were seen as a competing form of energy source as a threat that may replace or decrease the share of fossil fuels as an alternative energy resource in the US and developed countries. However, oil and gas industry has adapted to the wind of change and has started investing and utilizing the renewable sources of energy significantly. In this perspective, this study investigates and outlines the latest advances, technologies, potential of renewables both as an alternative and a complementary source of energy in the world n the current supply and demand dynamics of oil and gas resources. A comprehensive literature review focusing on the recent developments and findings in the renewable resources along with the availability of the renewable energy and locations are outlined and discussed under the current dynamics of the oil and gas market and resources. Literature review includes a broad spectrum that spans from technical petroleum literature with very comprehensive research using SCOPUS database to non-technical but renowned resources including journals and other publications including raw data as well as forecasts and opinions of respected experts. The raw data and expert opinions are organized, summarized and outlined in a temporal way within its category for the respective energy source. Not only the facts and information are outlined for the individual type of energy resource but also the relationship between the forms of energy resources are discussed from a perspective of their roles either as a competing or a complementary source to oil and gas. In this sense, this study goes beyond only providing raw data or facts about the energy resources but also a thorough publication that provides the oil and gas industry professional with a clear image of the past, present and the expected near future of the oil and gas industry as it stands with respect to renewable energy resources. Among the few existing studies that shed light on the current status of the oil and gas industry facing the development of the renewable energy are up-to-date and the existing studies within SPE domain focus on facts only lacking the interrelationship between the individual form of renewable energy and oil and gas such as solar energy used in oil and gas fields as a complementary renewable energy.

Abstract The revolution of smart well completions has been significantly enhancing the oil & gas industry in the recent years, The completions allow for higher PIs, better sweep, longer well life, longer reservoir contact and better water management. These effects came into play and needed once O&G industry moved to drilling multi-lateral wells. This paper represents a tri-lateral well that was drilled with high reservoir contact. The production optimization was completed to evaluate the contribution of each lateral and decide on the future production strategy for the well. This evaluation also allowed to test the functionality of the Down Hole Flow Control Valves (DHFCVs). Further, determining this functionality allowed identifying cross flow between the ICVs and the laterals. The optimization included multi-stage testing of each lateral to ascertain the high oil & water contributors. The water contribution was recorded across each lateral to optimize the water production and enhance the well productivity. The productivity index was calculated using IPR modeling utilizing Pipe-Sim software based on the commingled multi-rate tests. To further plan the way forward on the well production, a flowchart was established during the optimization operation to guide through the optimization process, identify each lateral water contribution, and production strategy after the operation. This optimization has resulted in a significant cost avoidance, avoiding coil tubing horizontal logging intervention operations in all the three laterals. The details of the testing stages scenarios and the recommendations of the production strategies will be shared in this paper.

Abstract This study aims to apply machine learning (ML) to make history matching (HM) process easier, faster, more accurate, and more reliable by determining whether Local Grid Refinement (LGR) with transmissibility multiplier is needed to history match gas-condensate wells producing from geologically complex reservoirs and determining how LGR should be set up to successfully history match those production wells. The main challenges for HM gas-condensate production from Hai Thach wells are large effect of condensate banking (condensate blockage), flow baffles by the sub-seismic fault network, complex reservoir distribution and connectivity, highly uncertain HIIP, and lack of PVT information for most reservoirs. In this study, ML was applied to analyze production data using synthetic samples generated by a very large number of compositional sector models so that the need for LGR could be identified before the HM process and the required LGR setup could also be determined. The proposed method helped provide better models in a much shorter time, and improved the efficiency and reliability of the dynamic modeling process. 500+ synthetic samples were generated using compositional sector models and divided into training and test sets. Supervised classification algorithms including logistic regression, Gaussian, Bernoulli, and multinomial Naïve Bayes, linear discriminant analysis, support vector machine, K-nearest neighbors, and Decision Tree as well as ANN were applied to the data sets to determine the need for using LGR in HM. The best algorithm was found to be the Decision Tree classifier, with 100% and 99% accuracy on the training and the test sets, respectively. The size of the LGR area could also be determined reasonably well at 89% and 87% accuracy on the training and the test sets, respectively. The range of the transmissibility multiplier could also be determined reasonably well at 97% and 91% accuracy on the training and the test sets, respectively. Moreover, the ML model was validated using actual production and HM data. A new method of applying ML in dynamic modeling and HM of challenging gas-condensate wells in geologically complex reservoirs has been successfully applied to the high-pressure high-temperature Hai Thach field offshore Vietnam. The proposed method helped reduce many trial and error simulation runs and provide better and more reliable dynamic models.

Abstract Natural Gas production and transportation are at risk of Gas hydrate plugging especially when in offshore environments where temperature is low and pressure is high. These plugs can eventually block the pipeline, increase back pressure, stop production and ultimately rupture gas pipelines. This study seeks to develops machine learning models after a kinetic inhibitor to predict the gas hydrate formation and pressure changes within the natural gas flow line. Green hydrate inhibitor A, B and C were obtained as plant extracts and applied in low dosages (0.01 wt.% to 0.1 wt.%) on a 12meter skid-mounted hydrate closed flow loop. From the data generated, the optimal dosages of inhibitor A, B and C were observed to be 0.02 wt.%, 0.06 wt.% and 0.1 wt.% respectively. The data associated with these optimal dosages were fed to a set of supervised machine learning algorithms (Extreme gradient boost, Gradient boost regressor and Linear regressor) and a deep learning algorithm (Artificial Neural Network). The output results from the set of supervised learning algorithms and Deep Learning algorithms were compared in terms of their accuracies in predicting the hydrate formation and the pressure within the natural gas flow line. All models had accuracies greater than 90%. This result show that the application Machine learning to solving flow assurance problems is viable. The results show that it is viable to apply machine learning algorithms to solve flow assurance problems, analyzing data and getting reports which can improve accuracy and speed of on-site decision making process.

ABSTRACT This project provides a new realistic solution for the accuracy of down hole torque measurements using the integration of the Artificial intelligence (AI) technology with the downhole challenges being faced while drilling deep and high deviated wells. The new estimates are based on surface measurements which have the major influence on the bit torque (downhole torque) values while drilling. Artificial intelligence technology and its related applications such as; artificial neural network (ANN), support vector machine (SVM) and adaptive neuro fuzzy interference system (ANFIS) will be utilized to predict and estimate accurate wellbore torque which will be applied effectively to prevent real time stuck pipe situation through a friendly user software which will maintain the downhole torque within the SAFE zone by controlling the unified surface drilling variables such as; weight on bit (WOB), Rate of Penetration (ROP) and Flow Rate. This downhole torque model will be validated and verified through a real drilling scenario from a field in north of Africa. The field data includes weight on bit, surface torque, stand-pipe pressure, and rate of penetration were collected from the mentioned well which had experienced a costly stuck pipe situation. However, with the provided model the same encountered scenario will be avoided, due to the optimization of the real time drilling variables and hence, saving the well and evade a costly non-productive time.

Abstract The formation porosity of drilled rock is an important parameter that determines the formation storage capacity. The common industrial technique for rock porosity acquisition is through the downhole logging tool. Usually logging while drilling, or wireline porosity logging provides a complete porosity log for the section of interest, however, the operational constraints for the logging tool might preclude the logging job, in addition to the job cost. The objective of this study is to provide an intelligent prediction model to predict the porosity from the drilling parameters. Artificial neural network (ANN) is a tool of artificial intelligence (AI) and it was employed in this study to build the porosity prediction model based on the drilling parameters as the weight on bit (WOB), drill string rotating-speed (RS), drilling torque (T), stand-pipe pressure (SPP), mud pumping rate (Q). The novel contribution of this study is to provide a rock porosity model for complex lithology formations using drilling parameters in real-time. The model was built using 2,700 data points from well (A) with 74:26 training to testing ratio. Many sensitivity analyses were performed to optimize the ANN model. The model was validated using unseen data set (1,000 data points) of Well (B), which is located in the same field and drilled across the same complex lithology. The results showed the high performance for the model either for training and testing or validation processes. The overall accuracy for the model was determined in terms of correlation coefficient (R) and average absolute percentage error (AAPE). Overall, R was higher than 0.91 and AAPE was less than 6.1 % for the model building and validation. Predicting the rock porosity while drilling in real-time will save the logging cost, and besides, will provide a guide for the formation storage capacity and interpretation analysis.

Abstract The usage of Artificial Intelligence (AI) in the arena of drilling optimization is a rapidly evolving endeavor and is becoming increasingly prevalent. In many applications the goal is process automation and optimization with the intent to reduce cost, improve yield/outcome and address risk. Real-world experience, however, has taught us that the correct application, configuration, and realtime management of an AI system is equally as important as the underlying algorithms. This paper poses that the implementation of an automated AI drilling system must consider the human element of acceptance in order to succeed. Proper onboarding and user acceptance is requisite to proper system configuration and performance. This paper sets forth guidelines that can be considered standard for initiating an AI drilling program.

The effective collaboration between various data scientists and domain experts is perhaps the most important, which is discussed here. Based on practical experience, the principal theses put forward here are that (1) data science projects require domain expertise, (2) domain expertise and data science expertise generally cannot be provided by the same individual, (3) effective communication between the various experts is essential for which everyone requires some limited understanding of the others' expertise and real-world experience, and (4) management must acknowledge these aspects by reserving sufficient project time and budget for communication and change management.

Abstract The petroleum industry has continued to show more interest in the application of artificial intelligence (AI). Most professional gatherings now have sub-themes to highlight AI applications. Similarly, the number of publications featuring AI applications has increased. The industry is facing the challenge of scaling up the applications to practical and impactful levels. Most of the applications end up in technical publications and narrow proofs of concept. For the industry's digital transformation objective to be fully achieved, efforts are required to overcome the current limitations. This paper discusses possible causes of the prevailing challenges and prescribes a number of recommendations to overcome them. The recommendations include ways to handle data shortage and unavailability issues, and how AI projects can be designed to provide more impactful solutions, regenerate missing or incomplete logs, and provide alternative workflows to estimate certain reservoir properties. The results of three successful applications are presented to demonstrate the efficacy of the recommendations. The first application estimates a log of reservoir rock cementation factors from wireline data to overcome the limitation of the conventional approach of using a constant value. The second application used the machine learning methodology to regenerate missing logs possibly due to tool failure or bad hole conditions. The third application provides an alternative approach to estimate reservoir rock grain size to overcome the challenges of the conventional core description. Tips on how these applications can be integrated to create a bigger impact on exploration and production (E&P) workflows are shared. It is hoped that this paper will enrich the current AI implementation strategy and practice. It will also encourage increased synergy and collaborative integration of domain expertise and AI methods to make better impact and achieve the digital transformation of E&P business goals.