Haider, Bader Y.A. (Kuwait Oil Company) | Rachapudi, Rama Rao Venkata Subba (Kuwait Oil Company) | Al-Yahya, Mohammad (Kuwait Oil Company) | Al-Mutairi, Talal (Kuwait Oil Company) | Al Deyain, Khaled Waleed (Kuwait Oil Company)
Production from Artificially lifted (ESP) well depends on the performance of ESP and reservoir inflow. Realtime monitoring of ESP performance and reservoir productivity is essential for production optimization and this in turn will help in improving the ESP run life. Realtime Workflow was developed to track the ESP performance and well productivity using Realtime ESP sensor data. This workflow was automated by using real time data server and results were made available through Desk top application.
Realtime ESP performance information was used in regular well reviews to identify the problems with ESP performance, to investigate the opportunity for increasing the production. Further ESP real time data combined with well model analysis was used in addressing well problems.
This paper describes about the workflow design, automation and real field case implementation of optimization decisions. Ultimately, this workflow helped in extending the ESP run life and created a well performance monitoring system that eliminated the manual maintenance of the data .In Future, this workflow will be part of full field Digital oil field implementation.
The well drainage pressure and radius are key parameters of real-time well and reservoir performance optimization, well test design and new wells' location identification. Currently, the primary method of estimating the well drainage radius is buildup tests and their subsequent well test analysis. Such buildup tests are conducted using wireline-run quartz gauges for an extended well shut-in period resulting in deferred production and risky operations.
A calculation method for predicting well/reservoir drainage pressure and radius is proposed based on single-downhole pressure gauge, flowing well parameters and PVT data. The proposed method uses a simple approach and applies established well testing equations on the flowing pressure and rates of a well to estimate its drainage parameters. This method of estimation is therefore not only desirable, but also necessary to eliminate shutting-in producing wells for extended periods; in addition to avoiding the cost and risk associated with the wireline operations. The results of this calculation method has been confirmed against measured downhole, shut-in pressure using wireline run gauges as well as dual gauge completed wells in addition to estimated well parameters from buildup tests.
This paper covers the procedure of the real-time estimation of the well/reservoir drainage pressure and radius in addition to an error estimation method between the measured and calculated parameters. Furthermore, the paper shows the value, applicability and validity of this technique through multiple examples.
A multilateral (MLT) well with an advanced intelligent completion string was recently completed in the Middle East. The well was designed as a "stacked?? dual producer in the upper and lower reservoir, and was drilled using the latest geo-steering techniques to accurately place the wellbore in a highly faulted and geologically complex structure. Rotary-steerable drilling systems (RSS) were used in several of the hole sections, along with advanced logging-while-drilling (LWD) tools including multi-pole acoustic, azimuthal deep resistivity, and resistivity at bit. Encounters with unstable shale and faults made the drilling difficult, but the decisions made in real-time to navigate the well resulted in a very high percentage of net pay in both laterals.
This well combined TAML Level 4 multilateral (MLT) technology with passive inflow control devices in the laterals and an advanced intelligent completion system in the mainbore. The TAML Level 4 multilateral junction was cemented to isolate unstable shale above the reservoir and to provide zonal isolation from the lateral completions, which were compartmentalized into stages with proprietary swellable packers and inflow control devices (ICDs). The intelligent completion was run in the mainbore with two interval control valves (ICVs) and isolation ball valve (LV ICV) to manage the production from each of the two laterals independently. The ICVs and LV ICV are controlled hydraulically through four control lines to surface, which were run in a flat-pack with one electric line to control a downhole gauge package for each lateral. Finally, the well was configured to allow the installation of a large electric submersible pump (ESP) to be run inside the upper 9-5/8-in. production tubing.
This project required intensive planning and coordination for more than a year in advance, which made the project successful despite the difficult drilling conditions and resulted in very little NPT for wellbore construction operations. This paper will focus on the planning, execution and lessons learned from the project.
In the existing horizontal wells in the target sand reservoir of the target field, premature water breakthrough caused the water cut trend to increase within months of production. . This occurred because the reservoir has a very high permeability sands along with active faults containing high viscous reservoir fluids.
New technologies were required to overcome the issue, maximize reservoir contact and enhance a more uniform oil production from a single location. Introducing the smart TAML Level-4 MLT well design to this reservoir along with inflow control device (ICD), inflow control valve (ICV), isolation ball valve (LV ICV) and other downhole gauges proved to be the optimum solution. It also aided in managing the production and the reservoir proactively to achieve maximum oil recovery. Moreover, drilling several laterals from a single wellbore with the ability to control production from both laterals had a great economic advantage because of the optimized cost effective field management.
The time taken to safely optimise a reservoir produced by artificial lift can be measured in weeks or months.
Typically the well by well process is as follows:
• Well testing
• Amalgamation of the well test data with down hole gauge and ESP controller data
• Analysis of the data to find the existing operation conditions
• Analysis of the ESP pump curve operating point and optimisation limitations
• Sensitivity studies in software to assess the optimum frequency and WHP
• Notification for the field operations to action the changes
• Further well tests to verify the new production data.
• Analysis of the data to ensure the ESP and well are running optimally and safely at the new set points
New technology enables this process to be performed in real time across the entire reservoir or field, significantly shortening the time to increased production and enabling real time reservoir management.
Each artificially lifted well in the reservoir was equipped with an intelligent data processing device programmed with a real time model of the well. The processors were linked to a central access point where the operation of field could be remotely viewed in real time.
Each well's processor was provided with a target bottom hole flowing pressure to enable the optimum production of the reservoir. The real time system automatically compared the desired target drawdown values with the capability of the pumping system installed in each well, and automatically suggested the optimum operating frequency and well head pressure to achieve the target. Where the lift system was not capable of producing to the target bottom hole pressure, a larger pump was automatically recommended. As production conditions change the system adapted its recommended operating points to compensate and maintain target production.
This paper discusses three case studies where real time optimisation and diagnosis lead to improved production from the reservoir.
Ali, Zaki (Schlumberger) | G. Bonilla, Juan Carlos (Schlumberger) | Zolotavin, Andrey (Kuwait Oil Company) | Al-Shammari, Reem Faraj (Kuwait Oil Company) | Robert, Herric (Schlumberger) | Saleem, Hussain A. (Kuwait Oil Company) | Farid, Ahmad (Schlumberger)
As oilfields mature and new fields come into operation, real time asset management of reserves is providing ongoing challenges to Kuwait Oil Company (KOC). Fewer engineers are managing more wells under increasingly tougher environmental conditions and compliance regulations. The combination of these factors has driven the need for KOC to make a step change in its approach to operations by incorporating digital field concepts to transform the way engineers are working. The result is the Kuwait Intelligent Digital Field initiative.
To enable KwIDF, new technologies were deployed in both mature and immature assets, creating issues in terms of interoperability and integration thereby increasing the strain on the legacy IT infrastructure. In addition, there was the requirement to isolate the SCADA industrial networks from the corporate business networks while automating traffic control with the various enterprise data systems. This ‘managed' separation complicated the delivery of productivity tools to employees and posed the greatest challenge to creating a transparent, seamless KwIDF infrastructure.
The KwIDF Jurassic project was particularly challenging since it had the most limited existing infrastructure, requiring the design and deployment of an entirely new architecture scattered over significant distances and business areas. This in turn created significant hurdles in terms of integration and compatibility with the remainder of KOC's proprietary systems and technologies. Specific efforts were required to allow KOC's network infrastructure to be capable of embracing such solutions and technologies with proper security measures in place.
Developing a network infrastructure to enable real time solutions for KwIDF Jurassic involved analyzing the specific business drivers of the asset to ensure that the capital investment not only delivered results, but did so within a secure environment. This paper presents the methodology employed by KOC's Corporate IT Group (CITG) to deliver the right network infrastructure, along with lessons learned, for enabling the Kuwait Intelligent Digital Field Jurassic project.
Vibrations are caused by bit and drill string interaction with formations under certain drilling conditions. They are affected by different parameters such as weight on bit, rotary speed, mud properties, BHA and bit design as well as by the mechanical properties of the formations. During the actual drilling process the bit interacts with different formation layers whereby each of those layers usually have different mechanical properties. Vibrations are also indirectly affected by the formations since weight on bit and rotary speed are usually optimized against changing formations (drilling optimization process). Therefore it can be concluded that for optimized drilling reduction of vibrations is one of the challenges.
A fully automated laboratory scale drilling rig, the CDC miniRig, has been used to conduct experimental tests. A three component vibration sensor sub attached to drill string records drill string vibrations and an additional sensor system records the drilling parameters. Uniform concrete cubes with different mechanical properties were built. Those cubes as well as a homogeneous sandstone cube were drilled with different ranges of weight on bit and bit rotary speed. The mechanical properties of all cubes were measured prior to the experiments. During all experiments, drilling parameters and the vibration data were recorded. Based on analyses of the data in the time and the frequency domain, linear and non-linear models were built. For this purpose the interrelations of sandstone and concrete mechanical properties, drilling parameters and vibration data were modeled by neural networks. Application of sophisticated attribute selection methods showed that vibration data in both, time- and frequency domain, have a major impact in modeling the rate of penetration.
Offshore production of heavy oil can be challenging due largely to adverse fluid properties, sand production and flow assurance concerns. Recent technology advancements effectively driving management of these challenges and government support through tax relief have significantly contributed to the increased appraisal activity over the last several years in the North Sea heavy oil fields. Application of appropriate technologies and techniques has always been of paramount importance for acquiring high quality information throughout welltest for reservoir characterization at appraisal stage of the fields. It also provides high level of confidence in technology and "proof of concept?? prior to further application in a full field development at investment intensive offshore operating environment.
This paper describes an integrated approach in analytical modeling and design developed and applied in the planning of flow test in a number of North Sea heavy oil fields. This includes a comprehensive pre-evaluation of well productivity, PVT properties modeling as well as design and selection of appropriate artificial lift method. A series of technical solutions considered relevant in relation to enhancing the low flowing well head temperature conditions, typically observed during the cold heavy oil production offshore and often leading to operational constraints on fluid handling capabilities is also discussed. Additionally, a probablistic approach considering base case, low and high case scenarios has been developed and implemented as part of the evaluation process, given the limited amount of available information and high level of uncertainties.
The study demonstrates the benefits of applying analytical techniques for uncertainties handling during flow test planning and thereby enabling accentuation of potential issues, properly planning for mitigation actions and predicting the entire flow test sequence. Finally the study underlines some important guidelines pertaining to planning for further appraisal and development of new heavy oil fields.
The directional drilling companies in oil industry usually provide well placement services using proprietary geosteering software that utilize conventional Logging-While-Drilling (LWD) data. Usually online access to the recorded logs is available to end users, but often very limited capability exists within the oil companies to test geosteering interpretations and advise. Present paper shares the case studies of some wells in which Gas-While-Drilling (GWD) data was used in conjunction with the LWD data for well placement. Furthermore, the Geosteering Module of a third party 3D Geological modeling software was used independently within the West Kuwait Fields Development group of KOC for well placement.
Well D-08 was drilled as vertical producer in a West Kuwait Marrat carbonate reservoir, produced economic quantities of oil during initial testing, but it started cutting high amount of water due to the effect of a fault. Therefore, the well was re-entered and sidetracked at a high angle, away from the fault. Similarly, the U-73 vertical well which encountered poor reservoir facies on flank of the field, was re-entered for productivity enhancement into a thin porous reservoir layer as horizontal sidetrack towards the crest. Both these wells were monitored and geosteered in near real-time using a geosteering software module which combines the overall structural framework provided by 3D geological model, along with the well log responses characteristics from offset wells, to produce a detailed pre-drill model for Geosteering. This is achieved by forward modeling to predict changes in log characters along the planned wellbore profile. The results are displayed both in vertical and measured depth domains along a 2D curtain section with formation tops parallel to the planned well azimuth.
In addition to the conventional LWD logs, the GWD logs generated from advanced gas analysis of the drilling mud were used for geosteering during drilling well D-08 and U-73 re-entry sidetrack wells. The LWD and GWD based geosteering were done independent of each other to test the efficacy of GWD method. Geosteering software and advanced mud gas data have been paired for high angle and horizontal well placement for the first time in Kuwait which successfully guided the well trajectory while drilling.
Arnaout, Arghad (TDE Thonhauser Data Engineering GmbH) | Thonhauser, Gerhard (Montanuniversitat Leoben) | Esmael, Bilal (Montanuniversitat Leoben) | Fruhwirth, Rudolf Konrad (TDE Thonhauser Data Engineering GmbH)
Detection of oilwell drilling operations is an important step for drilling process optimization. If drilling operations are classified accurately, detailed performance reports not only on drilling crews but also on drilling rigs can be produced. Using such reports, the management can evaluate the drilling work more precisely from performance point of view.
Mud-logging systems of modern drilling rigs provide numerous sensors data. Those sensors measurements are considered as indicators to monitor different states of drilling process. Usually real-time measurements of the following sensors data are available as surface measurements: hookload, block position, flow rates, pump pressure, borehole and bit depth, RPM, torque, rate of penetration and weight on bit.
In this work, collected sensors measurements from mud-logging systems are used to detect different drilling operations. Detailed data analysis shows that the surface sensors measurements can be considered as a main source of information about drilling operations. For this purpose, a mathematical model based on polynomials approximation is constructed to interpolate sensors data measurements.
Discrete polynomial moments are used as a tool to extract specific features (moments) from drilling sensors data. Then we use these moments for each drilling operation as pattern descriptor to classify similar operations in drilling time series. The extracted polynomial moments describe trends of sensors data and behavior of rig's sub-systems (Rotation System, Circulation System, and Hoisting System). Furthermore, this paper suggests a method on how to build patterns base and how to recognize and classify drilling operations once sensors data received from mud-logging system. Drilling experts compare the results to manually classified operations and the results show high accuracy.
Stuck pipe has been recognized as one of the most challenging and costly problems in the oil and gas industry. However, this problem can be treated proactively by predicting it before it occurs.
The purpose of this study is to implement the two most powerful machine learning methods, Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), to predict stuck pipe occurrences. Two developed models for ANNs and SVMs with different scenarios were implemented for prediction purposes. The models were designed and constructed by the MATLAB language. The MATLAB built-in functions of ANNs and SVMs, and the MATLAB interface from the library of support vector machines were applied to compare the results. Furthermore, one database that included mud properties, directional characteristics, and drilling parameters has been assembled for training and testing processes. The study involved classifying stuck pipe incidents into two groups - stuck and non-stuck - and also into three subgroups: differentially stuck, mechanically stuck, and non-stuck. This research has also gone through an optimization process which is vital in machine learning techniques to construct the most practical models. This study demonstrated that both ANNs and SVMs are able to predict stuck pipe occurrences with reasonable accuracy, over 85%.
The competitive SVM technique is able to generate generally reliable stuck pipe prediction. Besides, it can be found that SVMs are more convenient than ANNs since they need fewer parameters to be optimized. The constructed models generally apply very well in the areas for which they are built, but may not work for other areas. However, they are important especially when it comes to probability measures. Thus, they can be utilized with real-time data and would represent the results on a log viewer.