Well Cleanup Utilizing Smart Well Completion and Zero Flaring Technology

In an oil field, openhole multilateral maximum reservoir contact (MRC) wells are drilled. These wells are typically equipped with smart well completion technologies consisting of inflow control valves and permanent downhole monitoring systems. Conventional flowback techniques consisted of flowing back the well to atmosphere while burning the hydrocarbon and drilling fluids brought to surface. In an age of economic, environmental and safety consciousness, all practices in the petroleum industry are being examined closely. As such, the conventional method of flowing back wells is frowned upon from all aspects. This gives rise to the challenge of flowing back wells in an economic manner without compromising safety and the environment; all the while ensuring excellent well deliverability. By utilizing subsurface smart well completion inflow control valves, individual laterals are flowed to a separator system whereby solid drill cuttings are captured and discharged using a solids management system. Hydrocarbons are separated using a separation vessel and measured before being sent to the production line toward the field separation facility. Permanent downhole monitoring systems are used to monitor pressure drawdown and subsequently control the rate of flow to surface to ensure reservoir integrity. Following the completion of the solids and drilling fluid flowback from the wellbore, comprehensive multi-rate measurements at different choke settings are obtained to quantify the well performance. This paper looks at the economic and environmental improvements of the adopted zero flaring cleanup technology and smart well completions flowback techniques in comparison to conventional flowback methods. This ensures that oil is being recovered during well flowback and lateral contribution to overall flow in multilateral wells. In addition, it highlights the lessons learned and key best practices implemented during the cleanup operation to complete the job in a safe and efficient manner. This technique tends to set a roadmap for a better well flowback that fulfills economic constrains and protects the environment.

Tutorial: A Century of Sidewall Coring Evolution and Challenges, From Shallow Land to Deep Water

Due to the high cost of conventional coring operations, rotary sidewall coring has become increasingly important, particularly for deepwater operations. The rig costs, operational challenges, and amount of time involved to core wells below 30,000 ft are considerable, even for wireline operations. As wells get deeper, formation pressures will exceed 30,000 psi, and differential pressures can exceed 10,000 psi, which will eclipse the capabilities of traditional rotary coring tools. New technology has been introduced to enhance the recovery of rotary sidewall cores to improve operations and capabilities on these challenging wells that will be the primary subject of this paper. This new technology can also enhance coring operations and reliability for land and other offshore operations, in addition to deep water. New improvements and challenges include: * Reliable 1.5-in.-diameter core samples, with a 35,000-psi-rated tool * New high-powered coring tools with enhanced energy to address cutting Lower Tertiary wellcemented formations (Wilcox, Lower Miocene, etc.) * Higher torque and horsepower at the bit to enhance cutting and prevent stalling when coring * High-powered surface systems along with highstrength and high-power wireline cables * Upgrades to address high temperatures, highdifferential pressures, high-mud viscosity, large (24 in.) boreholes, and improved reliability * New drill bits and catcher rings to use a high-power system and operate in harsh coring environments * New cutting, retrieval, and core handling advancements for reliability in hard, consolidated formations * Combinability upgrades to reduce wireline trips and reduce rig costs for coring * Dual-coring tools with the ability to have different catcher rings and bits downhole simultaneously on a single run, along with tool redundancy downhole for improved reliability * Combination of rotary coring and formation sampling operations to obtain formation pressures, fluid samples, and rotary sidewall cores on a single run * Downhole monitoring of the coring operation, which includes drilling functions like torque, bit force, penetration rate, core bit penetration, and recovered core length, along with tool orientation * Core recovery information to enable 100% core verification downhole, so extra cores are not cut unnecessarily during the job, with individual core plugs measured and verified downhole * A unique method to seal the cores in a pressurecompensated coring tube downhole to capture all the formation fluids in the rock in downhole conditions * Complete rotary coring downhole operations can be monitored remotely for offsite interaction during the coring operation Besides reviewing historical coring tools and techniques, new technology is also discussed in more detail. The new technology starts with the introduction of the 1.5-in.-diameter rotary sidewall coring tools for deep water over a decade ago. Many applications and technologies are presented to show their effectiveness for deepwater operations. The successful examples include acquiring 1.5-in. cores in large boreholes, hard formations, deep wells, high-differential pressures, and extreme hydrostatic pressure. There are also examples of new technology available for future operations, including dual coring, combination coring, and sealed pressurized coring.

An Industry Overview of Downhole Monitoring Using Distributed Temperature Sensing: Fundamentals and Two Decades Deployment in Oil and Gas Industries

Distributed Temperature Sensing (DTS) system using optical fiber has been deployed for downhole monitoring over two-decades. Several technological advancements led to a wide acceptance of this technology as a reliable surveillance technique. This paper presents a comprehensive technical review of all the applications of the DTS, with focus on oil and gas industrial deployments. The paper starts with the advantages of the DTS over other methods and an overview of the DTS basics, including theory, the DTS components, deployment types, fiber types, design and limitations. Then, it is followed by the oil and gas applications of the DTS including hydraulic fracturing (during and after fracturing), well treatment/stimulation (acid injection, fluid distribution, diversion monitoring), inorganic (scaling) and organic (wax/asphaltene/hydrate) deposition detection, leak detection (in well and pipeline), flow monitoring (rate monitoring, water/steam injection and SAGD monitoring, CO2 storage monitoring, zonal contribution determination, gas lift optimization) and reservoir/fluid characterization (facies, porosity, permeability and fluid composition determination). This study reviews the historical development, applications and limitations of the DTS systems. The paper mainly focusses on deployment techniques, the application of the DTS for the prediction and surveillance of the non-thermal and thermal producer/injector wells, hydraulically fractured wells and those wells with treatments. The paper provides a concise review using several field cases from over two hundred published papers of Society of Petroleum Engineering (SPE) and journal databases. The application of the DTS in downhole monitoring can be divided into the qualitative and quantitative applications. In quantitative approaches, numerical models should be combined with the DTS data. This study discusses case by case worldwide field applications of DTS along with proposed modeling methods and interpretations. It also summarizes main challenges, including the fiber reliability, longevity, and operational limitations such as the installation and the complexity of quantitative approaches. This study is the foundation for an ongoing study on wellbore and reservoir surveillance through real-time distributed fiber optic sensing recordings along the wellbore. It summarizes the historical development and limitations to identify the existing gaps and reviews the lessons learned through the two decades of the application of the DTS in production performance.

Machine Learning for Deepwater Drilling: Gas-Kick-Alarm Classification Using Pilot-Scale Rig Data with Combined Surface-Riser-Downhole Monitoring

Summary Gas kicks occur frequently in deepwater drilling because of the extremely narrow mud-weight window [minimum 0.01 specific gravity (sg)]. The traditional kick-detection method mainly relies on the driller's analysis of monitored compound comprehensive mud-logging data. However, the traditional method has significant time lag, including missed and false detection, and often leads to severe gas influxes during deepwater drilling. A novel machine-learning (ML) model is presented here using pilot-scale rig data combined with surface-riser-downhole monitoring for gas-kick early detection and risk classification. A series of pilot-scaletest-well experiments (a total of 108 tests) are performed to simulate deepwater gas kicks and produce a multisource data set through fusion of comprehensive mud-logging data from surface monitoring, acoustic data from riser-monitoring technologies, and measurement-while-drilling data [e.g., bottomhole pressure (BHP)] from downhole monitoring technologies. During these experiments, the deepwater blowout preventer (BOP) is simulated using a variable cross section of crossover (X/O; equipped with booster-flow pipes); the Coriolis flowmeter is installed in the mud-return pipe to accurately measure flow out; the acoustic wave sensors are installed outside of the riser section (X/O) to monitor gas migration; and the downhole memory pressure gauges are installed to monitor BHP. Next, data preparation and data analysis are performed including raw-data exploration, data cleaning, signal/noise-ratio (SNR) analysis, feature scaling, outlier detection, and feature engineering. Further, a novel and improved data-labeling criterion for gas-kick alarms is proposed, with six levels (displayed using different colors) instead of two-state alarms (“kick” or “no kick”). The proposed gas-kick-alarm classification is in accordance with the actual field practices. Subsequently, four ML algorithms—decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and long short-term memory (LSTM)—are developed through the complete workflow, beginning with the data allocation and followed by building, evaluation, and optimization of each ML model. Because the LSTM recurrent neural network (RNN) algorithm showed the best performance, it is selected and deployed to early detect gas kicks and classify the corresponding kick alarms. The recall for gas-kick levels corresponding to Risk 0, Risk 1, Risk 2, Risk 3, Risk 4, and Risk 5 are 0.92, 0.93, 0.91, 0.91, 0.92 and 0.92, respectively. Because recall for each gas-kick-alarm level is greater than 0.9, it ensures rare false negatives (FNs) during kick detection. The accuracy, precision, recall, and f1 score of the deployed LSTM model in the testing data set is 91.6%, 0.93, 0.92 and 0.92, respectively. Further, the detection time delay is approximately 2 to 7 seconds only, which provides an improved time margin to take appropriate safety measures, promptly deal with a gas kick through a well-control program, and prevent a potential blowout during deepwater drilling.