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.
Gupta, M K (Oil and Natural Gas Corporation Ltd.) | Sukanandan, J N (Oil and Natural Gas Corporation Ltd.) | Singh, V K (Oil and Natural Gas Corporation Ltd.) | Bansal, R (Oil and Natural Gas Corporation Ltd.) | Pawar, A S (Oil and Natural Gas Corporation Ltd.) | Deuri, Budhin (Oil and Natural Gas Corporation Ltd.)
This paper discusses a case study of one of the onshore field of ONGC where while processing well fluid, frequent surge has been observed leading to shutdown of the SDVs creating severe operational problems and loss of production. It was imperative to find out the problematic wells/lines located in clusters which contribute for surge formation and mitigation approach with minimum modifications.
A transient complex network of sixty five wells flowing with a different lift mode such as intermittent gas lift, continuous gas lift etc were developed in a dynamic multiphase flow simulator OLGA. Time cycle of each well were introduced for intermittent lift wells. Simulation study reveals pulsating transient trends of liquid flow, pressure which was matched with the real time data of the plant and hence confirms the accuracy of the model. After verifying the results, different scenarios were created to determine the causes of surge formation. After finding the cause, a low cost approach was considered for surge mitigations.
An integrated rigorous simulation was carried out in OLGA, by feeding more than 12,000 data points to obtain model match. Several scenarios were also created such as optimization of lift gas quantity, optimization of elevation and size. Trend obtained after each scenario was pulsating behaviour and it matched with the real time data appearing in the SCADA system of the field. After rigorous simulation with each scenario, it was established that the cause of surge forming wells/pipelines. Once the root cause of surge has been confirmed then quantum of liquid generated due to surge was determined. Adequacy checks of the existing separators were carried out to estimate the handling capacity of the existing separators at prevalent operating condition. After adequacy check it was found that existing separators cannot handle the surge generated in that time interval leading to cross the high-high safety level, resulting closure of shut down valve (SDV). After establishment of root cause of the surge, a low cost solution with small modification in pipelines and control system/valves was adopted to arrest the surges. It was first of its kind simulation carried out for a huge network of wells/ pipelines by feeding more than 12,000 data to analyze the surge formation cause and capture its dynamism owing to wide array of suspected causes. This will help to address the challenges of efficiently reviewing the entire pipeline network while designing new well pad/GGS and will also help to arrest surge by adopting a low cost solution wherever such situation arises.
This paper presents a data driven approach to answer the question of whether premium, high strength white sand proppant, while more expensive than regional (brown) sand, is justified due to its alleged ability to make better producing wells. For this study, 739 horizontal wells with production, and stimulation data were used in a robust statistical approach to conclude that, for the most common set of well characteristics, white sand will produce a superior NPV weighted economic outcome than lower cost regional (brown) sand alternatives. While there are wells in this analysis that did not produce this robust conclusion of "white sand is better", none of them produced an outcome that "brown sand was better". Rather, several of the wells simply had results that were statistically inconclusive. This paper serves as a good example of what data are needed to perform such an analysis and the challenges of normalizing'first order effects' that dominate the influence on well productivity (TVD, lateral length, and proppant intensity) while attempting to ascertain the influence of'second order' factors such as Sand Type. Becoming familiar and adept at these analysis methods should facilitate the statistical verification of other second order effects on finding the optimal stimulation treatment.