ATCE is SPE’s annual meeting of members and features groundbreaking papers and special technical events designed to accelerate the application of innovations in every technical discipline. Attendees come from around the world to ATCE to keep up with the latest technologies, industry best practices, and new product launches.
The SPE Annual Technical Conference and Exhibition (ATCE) brings together thousands of E&P professionals and experts for learning, networking, and collaborating on the collective goals of the industry. A major highlight in 2018 was the Opening General Session focused on "Translating Big Data into Business Results" with key panelists from Shell, Encana, Schlumberger, and Google Cloud. Registration and housing information for the 2019 conference will be available in mid-June.
Hydrocarbons are trapped at great depths with pressure and temperature higher than surface conditions which would vary depending on reservoir properties. When the well is set on production, these hydrocarbons travel through the wellbore over reducing geothermal and formation pressure gradients. Hence, at shallower depths the temperature drops below the cloud point and sometimes, below pour point of crude thus creating an ambient temperature for the formation of wax and deposition of paraffin on the inner side of production tubing.
It has been observed that when hot fluid passes through a pipe which is covered by a continuously circulating hot water bath, the temperature difference of the fluid at surface outlet and sub-surface reservoir is reduced to a minimal value. This paper therefore proposes a practical application of such heat transfer within a wellbore for passively solving major industrial issues of paraffin depositions. The idea lies in minimizing the heat losses, which can be effectively done by insulating the inner side of the casing so that the annulus and fluid flowing within the tubing is isolated from exterior losses. According to the First law of Thermodynamics the fluid flowing within the tubing will experience reduction in thermal gradient. These loses can be compensated by injecting hotter brine through a pipe at the bottom of the annulus, which is isolated, using production packer. Further, circulating hot fluid in the annulus would result in isothermal heating of the fluid flowing through the tube which would minimize the heat loss across tubing, causing an increase in temperature of fluid at the surface above pour point. Several researchers have put forth heat transfer equations across the tubing's, annulus, insulator, casing, cement and the formation which can be used to calculate the overall heat transfer coefficient and thus, the amount of heat losses. Quartz sensors placed at the bottom of a wellbore would detect bottom borehole temperature based on which the injection temperature of fluid can be manipulated. The entire process can be automated by applying an artificial intelligent system which would monitor, control and respond. This method would increase the capex but would decrease the operating cost thus leading to an increase in the life of the well.
Temizel, Cenk (Aera Energy) | Balaji, Karthik (University of North Dakota) | Canbaz, Celal Hakan (Ege University) | Palabiyik, Yildiray (Istanbul Technical University) | Moreno, Raul (Smart Recovery) | Rabiei, Minou (University of North Dakota) | Zhou, Zifu (University of North Dakota) | Ranjith, Rahul (Far Technologies)
Due to complex characteristics of shale reservoirs, data-driven techniques offer fast and practical solutions in optimization and better management of shale assets. Developments in data-driven techniques enable robust analysis of not only the primary depletion mechanisms, but also the enhanced oil recovery in unconventionals such as natural gas injection. This study provides a comprehensive background on application of data-driven methods in oil and gas industry, the process, methodology and learnings along with examples of data-driven analysis of natural gas injection in shale oil reservoirs through the use of publicly-available data.
Data is obtained and organized. Patterns in production data are analyzed using data-driven methods to understand key parameters in the recovery process as well as the optimum operational strategies to improve recovery. The complete process is illustrated step-by-step for clarity and to serve as a practical guide for readers. This study also provides information on what other alternative physics-based evaluation methods will be able to offer in the current conditions of data availability and the understanding of physics of recovery in shale oil assets together with the comparison of outcomes of those methods with respect to the data-driven methods. Thereby, a thorough comparison of physics-based and data-driven methods, their advantages, drawbacks and challenges are provided.
It has been observed that data organization and filtering takes significant time before application of the actual data-driven method, yet data-driven methods serve as a practical solution in fields that are mature enough to bear data for analysis as long as the methodology is carefully applied. The advantages, challenges and associated risks of using data-driven methods are also included. The results of comparison between physics-based methods and data-driven methods illustrate the advantages and disadvantages of each method while providing the differences in evaluation and outcome along with a guideline for when to use what kind of strategy and evaluation in an asset.
A comprehensive understanding of the interactions between key components of the formation and the way various elements of an EOR process impact these interactions, is of paramount importance. Among the few existing studies on natural gas injection in shale oil with the use of data-driven methods in oil and gas industry include a comparative approach including the physics-based methods but lack the interrelationship between physics-based and data-driven methods as a complementary and a competitor within the era of rise of unconventionals. This study closes the gap and serves as an up-to-date reference for industry professionals.
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) | Alkhamis, Mohammed M. (Missouri University of Science and Technology) | Mutar, Rusul A. (Ministry of Communications and Technology)
Lost circulation is a complicated problem to be predicted with conventional statistical tools. As the drilling environment is getting more complicated nowadays, more advanced techniques such as artificial neural networks (ANNs) are required to help to estimate mud losses prior to drilling. The aim of this work is to estimate mud losses for induced fractures formations prior to drilling to assist the drilling personnel in preparing remedies for this problem prior to entering the losses zone. Once the severity of losses is known, the key drilling parameters can be adjusted to avoid or at least mitigate losses as a proactive approach.
Lost circulation data were extracted from over 1500 wells drilled worldwide. The data were divided into three sets; training, validation, and testing datasets. 60% of the data are used for training, 20% for validation, and 20% for testing. Any ANN consists of the following layers, the input layer, hidden layer(s), and the output layer. A determination of the optimum number of hidden layers and the number of neurons in each hidden layer is required to have the best estimation, this is done using the mean square of error (MSE). A supervised ANNs was created for induced fractures formations. A decision was made to have one hidden layer in the network with ten neurons in the hidden layer. Since there are many training algorithms to choose from, it was necessary to choose the best algorithm for this specific data set. Ten different training algorithms were tested, the Levenberg-Marquardt (LM) algorithm was chosen since it gave the lowest MSE and it had the highest R-squared. The final results showed that the supervised ANN has the ability to predict lost circulation with an overall R-squared of 0.925 for induced fractures formations. This is a very good estimation that will help the drilling personnel prepare remedies before entering the losses zone as well as adjusting the key drilling parameters to avoid or at least mitigate losses as a proactive approach. This ANN can be used globally for any induced fractures formations that are suffering from the lost circulation problem to estimate mud losses.
As the demand for energy increases, the drilling process is becoming more challenging. Thus, more advanced tools such as ANNs are required to better tackle these problems. The ANN built in this paper can be adapted to commercial software that predicts lost circulation for any induced fractures formations globally.
This paper presents design, testing, installation, and lessons learned with the world's first completely integrated managed pressure drilling (MPD) control system on a deepwater drilling rig. While previous MPD installations have included driller-operated systems, they all include additional human machine interfaces (HMI) and standalone control network components with limited use of rig data and limited to no interfaces to other critical drilling machines on the drilling rig. For the installation described in this paper, all MPD control functions were permanently installed on the main drilling control network of the drilling unit, providing direct access to high speed data from other drilling machines that influence the wellbore pressure. This includes the rig's mud pumps, top drive, and drawworks. Moreover, the MPD control system has the ability to actively control the drilling machines, thereby optimizing performance through coordinated control of mud pump, top drive, and MPD chokes during drilling and connections.
In recent years, the oil and gas industry has gained greater operational efficiencies and productivity by deploying advanced technologies, such as smart sensors, data analytics, artificial intelligence and machine learning — all linked via Internet of Things connectivity. This transformation is profound, but just starting. Leading offshore E&P operators envision using such applications to help drive their production costs to as low as $7 per barrel or less. A large North Sea operator among them successfully deployed a low-manned platform in the Ivar Aasen field in December 2016, operating it via redundant control rooms — one on the platform, the other onshore 1,000 kilometers away in Trondheim, Norway. In January 2019, the offshore control room operators handed over the platform's control to the onshore operators, and it is now managed exclusively from the onshore one. One particular application — remote condition monitoring of equipment — supports a proactive, more predictive condition-based maintenance program, which is helping to ensure equipment availability, maximize utilization, and find ways to improve performance. This paper will explain the use case in greater detail, including insights into how artificial intelligence and machine learning are incorporated into this operational model. Also described will be the application of a closed-loop lifecycle platform management model, using the concepts of digital twins from pre-FEED and FEED phases through construction, commissioning, and an expected lifecycle spanning 20 years of operations. It is derived from an update to a paper presented at the 2018 SPE Offshore Technology Conference (OTC) that introduced the use case in its 2017-18 operating model, but that was before the debut of the platform's exclusive monitoring of its operations by its onshore control room.
Computer vision (CV) techniques were applied to X-ray computed tomographic (CT) images of reservoir cores to evaluate their potential for rapidly identifying fractures and other lithologic/geologic characteristics. The analyses utilized feature-labeled CT cross sectional images, themselves distributed submillimeter voxels of density- and atomic number-sensitive CT Numbers, as inputs to a Fully Convolutional Neural Network (FCN) for semantic segmentation of reservoir core features. In FCN, an image is interrogated using a series of sliding windows at various scales to create weighted filters to reduce error between classes in training images. These networks of filter layers were used to assign probabilities of classes, which were upscaled back to the original image dimensions resulting in probabilistic class assignments onto each pixel. FCN model accuracy, defined by its ability to replicate manually-assigned labels in the raw (unannotated) training image stack, was at least 80% and generally improved with the size of the training set. Once the labels were assigned, the underlying feature frequency, orientation, and size were measured in 3D volume reconstructions using algorithms modified from standard image analysis software. This method allowed users to endow a classification model with subject matter knowledge for further, autonomous label prediction. Thus, while initial image annotation was labor intensive, subsequent images were rapidly classified once the model was built. The classified labels were analyzed for abundance, orientation, and size of fractures were calculated to characterize spatial information of these features. FCN combined with fracture labeling improved knowledge capture and automation of fracture identification. Models trained by high quality 3D datasets can greatly reduce the time needed to describe subsequent core. The method demonstrated is not limited to fractures, other lithologic/geologic features could be trained using the same method, which may result in additional efficiencies.
In recent years, an industry-wide demand for increased drilling efficiency has led to the development of technologies and methods focused on multi-well pad development and the minimization of the transportation of drilling rigs between locations. Studies have indicated the potential for improving drilling cycle efficiency through improvements in rig design and procedural documentation but have given limited consideration to the unitization and mobilization practices surrounding ancillary components such as mud pumps, light plants, bulk fluid storage and other systems that comprise modern land rigs. This study examines current unitization practices, as well as offers alternative methods of integrating ancillary system components to improve current transport configurations. Specifically, ancillary systems whose transport dimensions and weight exceed the federal and state requirements for commercial vehicles operating within the National Highway Freight Network (NHFN).
In this study, the application of transport logistics software is used to demonstrate that there exists the potential for significant reduction in land rig mobilization costs through revised unitization of drilling rig ancillary systems. Permit data from proposed wells located in the Permian, Bakken, and Marcellus are utilized to develop transport scenarios whose focus is to quantify the impact of ancillary system unitization on the total fee structure associated with rig mobilization between geographical regions. Within each scenario, ancillary systems from currently active rigs are compiled and itemized according to their weight, transport dimensions, and degree of component unitization. The resulting schedule is then processed through transport logistics software to identify fee schedules associated with oversize permits, overweight permits, civilian and police escorts, driver rate/fuel costs, and associated service fees for the individual loads. Following the conclusions derived from the analysis of the existing rig systems, the series of transport scenarios are repeated using revised component configurations. The revised system employs a combination of divisible and non-divisible loads whose components are either integrated as part of dedicated transport trailers or located within ISO containers loaded onto commercially available transport trailers. The fee schedules from active rigs, as well as the results from the proposed unitization, are explored in detail to identify critical areas for improvement regarding unitization practices for active rigs and future builds.