This paper will focus on the application of lithium-ion energy storage solutions (ESS) for offshore oil and gas (O&G) installations. It will discuss the benefits that can be achieved by integrating energy storage in hybrid power plants, using the West Mira semisubmersible installation in the North Sea as a representative case study. West Mira will be the world's first modern drilling rig to operate a low-emission hybrid (dieselelectric) power plant using lithium-ion batteries. The integration of energy storage with the power supply and distribution system of a drilling rig represents an important step towards improving the environmental sustainability of the offshore oil and gas industry by reducing emissions and paving the way to harnessing clean but intermittent renewables, such as offshore wind. Offshore rigs have highly variable power consumption for drilling and dynamic positioning. By incorporating energy storage, it is possible to reduce the runtime of combustion engines and also keep them operating on an optimized combustion level. The installation of an ESS on West Mira will result in an estimated 42% reduction in the runtime of on-platform diesel engines, reducing CO2 emissions by 15 percent and NOx emissions by 12 percent, which is equivalent to annual emissions from approximately 10,000 automobiles. The batteries on West Mira will be charged from the rig's diesel-electric generators and used for supplying power during peak load times. In addition, they will serve as backup to prevent blackout situations and provide power to the thrusters in the unlikely event of loss of all running machinery.
Video images have traditionally provided intuitive visual analysis in a wide range of wellbore diagnostic situations. Step changes in computer vision techniques and image processing have led to the ability to make measurements from images (visual analytics). This paper demonstrates several applications where the application of this new data analytics source, combined with state-of-the-art acquisition technology, have further improved understanding of complex well issues while reducing operational time, risk and cost. Examples include hydraulic fracturing, well integrity, erosion, restrictions and leaks. The paper will describe the methods and process of this visual analytics technique through discussion of the three main work flow stages from data acquisition to final analytical product, including the innovative developments in sensor, system and computer vision applications that support each step: 1. Acquisition of full circumferential, depth-synchronized video data of the wellbore. An array of four orthogonally positioned cameras, pointing directly at the pipe wall, concurrently record overlapping images, enabling a continuous full-well video dataset to be obtained.
Makwashi, Nura (Division of Chemical and Petroleum Engineering, London South Bank University) | Barros, Delcia Soraia David (Division of Chemical and Petroleum Engineering, London South Bank University) | Sarkodie, Kwame (Division of Chemical and Petroleum Engineering, London South Bank University) | Zhao, Donglin (Division of Chemical and Petroleum Engineering, London South Bank University) | Diaz, Pedro A. (Division of Chemical and Petroleum Engineering, London South Bank University)
Production, transportation and storage of highly waxy crude oil is very challenging. This is because they are usually characterised by high content of macro-crystalline waxes, predominantly consisting of n-alkanes (C18 to C36) that which could cause costly deposition within the wellbore and production equipment. The accumulation of deposited wax can decrease oil production rates, cause equipment breakdown, and clog the transport and storage facilities. Currently, different polymeric inhibitors have been utilised in the oil and gas field to mitigate and prevent wax deposition. However, as of today, there is no distinctive wax inhibitor that could work effectively for all oil fields. One of the objectives of this work is to study the efficacy of a blended commercial wax inhibitor - pour point depressant on wax deposition mitigation in a flow rig designed with 0 and 45-degree bends in the pipeline.
Standard laboratory techniques using high-temperature gas chromatography (HTGC), rheometer rig, polarized microscope and elution chromatography were employed to obtain n-paraffin distribution, oil viscosity, WAT, pour point and SARA fractions. Series of experimentation were carried out with and without the inhibitor in a straight pipe test section. The severity of wax deposition in the pipeline built-in with a 45-degree bend is compared with a straight pipe. The blended inhibitor was tested at concentrations of 500, 1000, and 1500-ppm, under laminar and turbulent conditions. The crude oil sample was found to be naturally waxy with wax content of 19.75wt%, n-paraffin distributions ranges from C15-C74, WAT and pour point of 30°C and 25°C respectively. The severity of wax deposition in the test section is 43% higher in 45-degree bend compared to straight pipe. However, the severity of the deposition was reduced to 12.3% at extremely low temperature and flow rate. Nonetheless, better inhibition performance was achieved at 25 and 30°C. The wax thickness was reduced from
There are significant trends in the reduction of traditional 2D design, this is being replaced by the sole development of 3D models. This paper will detail how to develop algorithms to automate large aspects of a design review. These techniques significantly increase efficiency, ensure constancy and optimise the accuracy of the design, leading to reduced project costs.
Utilising the 3D models enriched metadata and by developing independent algorithms, it is possible to create a cyberphysical model that enables automation of the design review. For example; using the geometrical data in the 3D model to check a hazard with respect to a detector, confirming that the detector is located close to the hazard. There are multiple checks similar this example, cataloguing and scripting these checks can be managed within PLM software.
Using algorithmic automation techniques reduces the overall design hours of a project, it checks the consistency of the design. Getting it right first time reduces the number of changes later in the project lifecycle, avoiding expensive rework costs. During the first phase of this initiative, we have found, that automation leads to a reduction of design hours by 10% and increases the accuracy and consistency of the design review.
This first phase of automation uses the metadata in the 3D model, where the output from the check leads to a comment on the design. To scale the pilotm which will encompass the inclusion of other data sources, will further enrich the cyberphysical model. Ultimately, by creating a decisions database and using Artificial Intelligence we will be able to close the loop, which will lead to a design that is fully evaluated before it leaves the designer. It is also possible to automate in other phases of the project lifecycle, where image recognition will compare the real asset to the model.
This level of automation is unique, there are other low-level forms of automation, but the advancements of this technology has, to our knowledge, not been attempted in the Oil and Gas sector. The development and scaling of this technology is novel and will have a significant impact on the way future projects are executed.
Offshore oil and gas installations are (by their nature) located in remote locations that are both difficult and costly to access. While such challenges exist, the operate & maintain requirements associated with such assets are consistent and must be addressed, requiring operators to identify the most efficient form of service to reduce staffing levels, risk and cost.
Offshore hydrocarbon production assets commonly incorporate equipment and processes that can lead to significant (fugitive) gas emissions. The consequences are both economic and social (environmental) in nature, requiring operators to perform emissions surveys with the objective of leak identification and remediation within the shortest possible timeframe. The frequency of this activity is naturally limited and must be balanced with the staffing and operating needs of the broader facility, which in-turn can lead to sub-optimal leak detection to fix timing and reliability.
Addressing the three key challenges of access productivity, detection reliability and results quantification, Worley has developed a remote sensing platform that incorporates the use of productive remote access equipment such as unmanned aerial vehicles (UAV) and in-situ monitoring, with machine based emissions detection and algorithmic quantification to provide a solution that allows the operator to increase survey frequency, obtain more reliable results at lower cost, and perform the work in a manner consistent with safe and low-risk operations.
In both testing and field deployments, the results have provided for significant reductions in both false positive and negatives and have produced datasets that allow for accurate indications of greenhouse gas reduction via comparison of volumetric emissions before and after leak repair activity has taken place.
The technology is largely mathematical, utilizing coded routines for machine learning to perform gas detection under (initially) supervised modeling conditions, and algorithmic gas dispersion models for further emission quantification. The performance of the survey is typically carried out through the integration of existing, proven manufactured sensing equipment across several types of UAV or in-situ monitors which collect field data for transmission to a cloud-based portal which further processes the results.
The approach has been shown effective in accessing hard or costly to reach areas, improving survey productivities, while the data processing and quantification allows the operator to benefit from improved measurability and prioritize leak repair accordingly.
This paper explains how half a billion hours of service data for pressure safety valves (PSVs) can be analysed and presented to allow optimisation of maintenance intervals and manage the inspection of PSVs. The large amount of data creates challenges, but also opportunities for an enhanced methodology expandable to other equipment types.
A methodology has been developed that uniquely combines qualitative and quantitative analysis. The latter ensures that the risk from PSV failure is determined and below set criteria. The drawback with this criterion alone would be that it is based on average data from a large set of PSVs and the average may not be applicable everywhere especially if failures are not random, but have an underlying, potentially unknown, cause. Therefore, each PSV is also qualitatively assessed. To bridge the gap between individual PSVs and "large sets", groups of PSVs are also identified in the methodology and data for them is collated and used within the whole group; the careful analysis required to define the groups, which must have similar properties, or performance, is described. This multi-layered assessment gains the most information possible from the data. The key part of the process is also to present this data for review and analysis, and this is achieved through a digital, cloud-based interactive dashboard.
The analysis has shown that maintenance intervals can be reduced significantly, but simultaneously risk reduced by concentrating effort on the worst-performing PSVs. Not least, a dashboard presentation of the risk-based inspection (RBI) showing the calculated inspection interval, changes to the intervals and failures allows a clear picture to be developed of PSV performance. Maintenance planning also becomes easier and information required for deferral assessments is available in seconds rather than hours. The analysis shows where poorer performance can occur, this is often applicable across different assets. The way in which the approach can be expanded to other equipment types will be described.
The novel approach in the assessment is the multi-layered combination of qualitative and quantitative analysis and the presentation of a large amount of data through the cloud to be used by maintenance teams, technical authorities and operators. It also shows the benefits of collecting half a billion service hours of data and that this need not be an onerous task.
A Digital Twin is a software representation of a facility which can be used to understand, predict, and optimize performance to help to achieve top performance and recover future operational losses. The Digital twin consists of three components: a process model, a set of control algorithms, and knowledge.
Usually the time for commissioning a project exceeds the initial estimations, therefore delays in project completion are quite common. This is often because ICSS testing is done on a static system which does not account for how the system will react dynamically to certain scenarios such as start-ups and shutdowns. Issues such as configuration errors, loop behaviors, start-up over-rides, dead-lock inter-trips and sequence logic are difficult to predict and are impossible to anticipate during static testing. Such delays lead to higher costs and therefore reduced revenue.
This paper aims to describe the most innovative approach to Project & Operational Certainty, which addresses these issues by using a Digital Twin for commissioning support and training. One successful use of this approach was in the Culzean project, an ultra-high-pressure high temperature (UHP/HT) gas condensate development in the UK sector of the Central North Sea. A high-fidelity process model was built and fitted to the actual plant performance based on equipment data sheets. This was connected to ICSS database and graphics, offering a realistic environment, very close to the one offshore, which had the same look and feel for the operators.
Dynamic tests conducted on the Digital Twin predicted issues on the real system, which enabled potential solutions to be tested, leading to a significant decrease in the time spent and cost during commissioning. All the operating procedures were dynamically tested, which enabled us to correct errors, saving time before First Gas. Additionally, all CRO (Control Room Operators) and field technicians were trained and made familiar with the system months in advance, aiming to avoid future unnecessary trips during First Gas.
Finally, all the control loops were fine tuned in the Digital Twin and parameters were passed to off shore, to be used as first starting point. It is expected that these parameters will be very close to fine operational points, as the model used is high fidelity model and very close to real system offshore.
Recently two multilateral horizontal wells have been completed offshore using dedicated multistage hydraulic fracturing completions. The first well, located in the Central North Sea (referred to as ML-CNS), was stimulated using acid fracturing; while the second well, located in the Black Sea (referred to as ML-BKS), was stimulated using proppant fracturing. This paper presents the different drivers, challenges and lessons learned for each well while emphasizing the well construction and stimulation methodologies developed for the different reservoirs and field characteristics.
The field development drivers for drilling and completing these offshore hydraulic fractured multilateral wells, a first of their kind globally, was different for each case. The objective of the first project, initially considered uneconomic, was to engineer a technical solution for completion and production of two separate reservoirs with only one subsea well. The second project was seeking to optimize infill drilling from the last available slot on the offshore platform to maximize reservoir contact and production in the same reservoir. ML-CNS was a TAML Level 2 completion with a 14-stage, 5 ½" multistage completion run in each lateral and set-up for sequential acid fracturing. Operationally, the first lateral was drilled and stimulated, followed by the drilling and stimulation of the second lateral, using the drilling whipstock to navigate through the multilateral junction. ML-BKS was a TAML Level 3 completion that had a 6-stage, 4 ½" multistage completion installed in each lateral, which were proppant fractured following a sequence designed to minimize the jack-up rig time required. Both legs were drilled and completed prior to starting the stimulation, access to either lateral was achieved with the existing workover unit on the platform by manipulating a custom designed BHA.
The lessons learned from the first project executed in the North Sea were able to be transferred and applied to the second project in the Black Sea to allow for a more efficient and confident completion solution. Led by varying economical and regional constraints, the key factor for both wells centered on delivering operationally simple and reliable multilateral completion designs to economically meet the field development strategy in place.
To the knowledge of the authors and following subsequent literature research, both wells are a worldwide first for an offshore multilateral well completed with multistage acid fracturing and multistage proppant fracturing, and together they represent a new trend in cost-effective offshore field development through well stimulation. The successful case studies for both wells with the combined analysis of the benefits, challenges, and lessons learned will provide a guide and instill confidence with operators who find this approach beneficial with a view to applying it in other assets.
In 2016 BP adopted a technology plan to investigate how efficiencies could be realized in the inspection area. The project termed UWIP (Under Water Inspection Program) was divided into two areas: Alternative inspection technology, Advanced inspection technology.
Alternative Inspection technology addresses the configuration of existing technology to deliver efficiencies
Advanced Inspection technology looks to near future opportunities that may be realized within a 5-year period.
This presentation primarily addresses the Alternative agenda, with focus on how the configuring of sensor packages onboard a variety of underwater vehicles has delivered data up to 8 times faster than traditional inspection methodologies. Termed FDII (Fast Digital Imaging Inspection) the concept aims to replace video with Laser / Stills and contact Cathodic Potential systems with Field Gradient.
The Advanced agenda presents BP progress in delivering unmanned, automated Unmanned Surface and Underwater Vehicle Systems into Inspection programs.
BP has undertaken three FDII campaigns, 2017/18 in North Sea and 2018 Trinidad, inspecting 825 pipeline kilometers. There are another two FDII programs scheduled in North Sea and Caspian regions in 2019. Data acquisition has significantly increased; however, data management techniques have had to be reviewed and adapted. Inspection and integrity contractors expect to receive data in traditional formats and their systems (as well as operators) are not configured to receive and interpret the new FDII data. Additionally, software houses are also behind the curve in allowing users to host and deliver to stakeholders.
FDII facilitates rapid data acquisition and operational teams are ready to grab credit for efficient execution. But data bottlenecks in editing, eventing and delivering data to stakeholders have removed some of the ‘shine’ from the project. For FDII to develop a step change is required in the data management.
FDII is a technique, it is not an inspection criterion. FDII lends itself to Fast ROV and AUV underwater vehicle developments which are also linked to operation from Unmanned Surface Vessels. BP has a stated goal that by 2025 all inspections will performed from unmanned systems. FDII is a technology that progresses us to that goal.
Glover, Paul W. J. (University of Leeds) | Lorinczi, Piroska (University of Leeds) | Al-Zainaldin, Saud (University of Leeds) | Al-Ramadhan, Hassan (University of Leeds) | Sinan, Saddam (University of Leeds) | Daniel, George (University of Leeds)
New reservoirs are increasingly more heterogeneous and more anisotropic. Unfortunately, conventional reservoir modelling has a resolution of only about 50 m, which means it cannot be used to model heterogeneous and anisotropic reservoirs effectively when such reservoirs exhibit significant inter-well variability at scales less than 50 m. This paper describes a new fractal approach to the modelling and simulation of heterogeneous and anisotropic reservoirs. This approach includes data at all scales such that it can represent the heterogeneity of the reservoir correctly at each scale.
Three-dimensional Advanced Fractal Reservoir Models (AFRMs) can be generated easily with the appropriate code. This paper will show: (i) how 3D AFRMs can be generated and normalised to represent key petrophysical parameters, (ii) how these models can be used to calculate permeability, synthetic poro-perm cross-plots, water saturation maps and relative permeability curves, (iii) the effect of altering controlled heterogeneity and anisotropy of generic models on fluid production parameters, and (iv) how AFRMs which have been conditioned to represent real reservoirs provide a much better simulated production parameters than the current best technology.
Results of generic modelling and simulation with AFRMs show how total hydrocarbon production, hydrocarbon production rate, water cut and the time to water breakthrough all depend strongly both on heterogeneity and anisotropy. The results also show that in heterogeneous reservoirs, the best production data is obtained from placing both injectors and producers in the most permeable areas of the reservoir – a result which is at variance with common practice. Modelling with different degrees and directions of anisotropy shows how critical hydrocarbon production data depends on the direction of the anisotropy, and how that changes over the lifetime of the reservoir.
We have developed a method of fractal interpolation to condition AFRMs to real reservoirs across a wide scale range. Comparison of the hydrocarbon production characteristics of such an approach to a conventional krigging shows a remarkable improvement in the modelling of hydrocarbon production when AFRMs are used; with AFRMs in moderate and high heterogeneity reservoirs returning values always within 5% of the reference case, while the conventional approach often resulted in systematic underestimations of production rate by over 70%.