Difference between revisions of "Remote Operated Processing Platform"

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=Technology Roadmap Sections and Deliverables=
=Technology Roadmap Sections and Deliverables=


Info on Remote Oil and Gas Processing Platform
On an offshore oil and gas platform, operators are physically located on the facility to provide local intervention with a goal of ensuring safe, reliable processing capabilities.  Introduction of a remote operated platform removes operations and maintenance personnel from being physically located on the platform to a central operating location where the focus is on real-time monitoring and intervention is achieved.  It is worth noting that this is primary a systems integration and scale-up issue.  The vast majority of the underlying technology has been proven across different applications, but never integrated into a new large deepwater development. <br>
<br>
The unique identifier outlined in the roadmap is that of a remote operated oil and gas processing platform, level 5 - '''ROPP5'''


==Roadmap Overview==
==Roadmap Overview==


The working principle and architecture of a remote operated oil and gas processing platform is depicted in the below.  
The high-level working principle and architecture of a remote operated oil and gas processing platform is depicted in the below. The core infrastructure of any offshore oil and gas processing facility consists of an inlet well fluid stream and initial separation followed by oil, water, and gas processing streams.  Utilities support all processing streams and include main power, instrument air, heating and cooling medium, fresh and seawater supply to name a few. 


[[File:RdmpOverview.jpg|frameless]]
[[File:RdmpOverview.jpg|500px|frameless]]


details
As of today, these processes are more or less controlled via central control room on a production facility with manual intervention by operations and maintenance personnel, as required.  The elimination of onsite personnel is depicted in our overview to highlight a remote control center operated via cloud computing technology.


==Design Structure Matrix (DSM) Allocation==
==Design Structure Matrix (DSM) Allocation==


uploadDSM
We can extract from the DSM below that the remote operated processing platform is composed of and dependent on a series of decisions and technologies.  These decisions and subsequent technologies include facility location, communication and control method, process design, monitoring and intervention.


The 2-SEA tree that we can extract from the DSM above shows us that the Solar-Electric Aircraft (2SEA) is part of a larger company-wide initiative on electrification of flight (1ELE), and that it requires the following key enabling technologies at the subsystem level: 3CFP Carbon Fiber Polymers, 3HEP Hybrid Electric Propulsion and 3EPS Non-Propulsive Energy Management (e.g. this includes the management of the charge-discharge cycle of the batteries during the day-night cycle). In turn these require enabling technologies at level 4, the technology component level: 4CMP components made from CFRP (spars, wing box, fairings …), 4EMT electric machines (motors and generators), 4ENS energy sources (such as thin film photovoltaics bonded to flight surfaces) and 4STO (energy storage in the form of lithium-type batteries).
[[File:UpdateRODSM.jpg|600px|frameless|Remote Operated Platform DSM]]
 
Level 1: Communication and structural design, dependent on reservoir location - Platform design: '''PD1''' <br>
Level 2: Facility design, dependent on well fluid characteristics - Facility design:  '''FD2''' <br>
Level 3: Independent, remote monitoring - Remote monitoring design:  '''RMD3''' <br>
Level 4: Monitoring, dependent on facility operating design - Operating design:  '''SOD4''' <br>
Level 5: Remote operated platform, dependent on location, well fluid characteristics, and monitoring - Remote Operated Processing Platform:  '''ROPP5'''


==Roadmap Model using OPM==
==Roadmap Model using OPM==
We provide an Object-Process-Diagram (OPD)  of the 2SEA roadmap in the figure below. This diagram captures the main object of the roadmap (Solar-Electric Aircraft), its various instances including main competitors, its decomposition into subsystems (wing, battery, e-motor …), its characterization by Figures of Merit (FOMs) as well as the main processes (Flying, Recharging).
We provide an Object-Process-Diagram (OPD)  of the in the figure below. This diagram captures the main object of the roadmap (oil platform), its various processes required for transferring of information to control the facility remotely, as well as the Figures of Merit and main processes.  


[[File:Section 3.JPG]]


An Object-Process-Language (OPL) description of the roadmap scope is auto-generated and given below. It reflects the same content as the previous figure, but in a formal natural language.  
[[File:OPD Unmanned Rev1.jpg|1000px|frameless]]
 
An Object-Process-Language (OPL) description of the roadmap scope is auto-generated via Opcloud and provided below. This is simply an export of the OPD in a formal natural language.  
 
[[File:OPL Rev 3.jpg|600px|frameless]]


[[File:Section 3_2.JPG]]


==Figures of Merit==
==Figures of Merit==
The table below show a list of FOMs by which solar electric aircraft can be assessed. The first four (shown in bold) are used to assess the aircraft itself. They are very similar to the FOMs that are used to compare traditional aircraft which are propelled by fossil fuels, the big difference being that 2SEA is essentially emissions free during flight operations. The other rows represent subordinated FOMs which impact the performance and cost of solar electric aircraft but are provided as outputs (primary FOMs) from lower level roadmaps at level 3 or level 4, see the DSM above.


[[File:Section 4_.JPG]]
The table below show a list of FOMs by which a remote operated processing platform can be assessed. The best indicator of the profitability of any oil and gas development is the unit cost, which is the capital required to produce the barrels of oil in the development and the unit operating expense.  This is a strong function of the People on Board, but the information is highly confidential and cannot be shared publicly.  It is shown as f(People on Board) in the equations to maintain confidentiality.  Any sustaining technology will need to be competitive within the capital and operating cost per barrel before other considerations.  The main consideration outside of cost is safety and environment.  Focusing on safety as measured through injuries per 1000 hrs worked, the simplest way to reduce the safety incidents is to remove the people.  Removing people is the focus of this technology.  Our measure is Design People on Board (POB) per throughput to normalized for the size of the development.  When plotted with historical data we see a clear trend with throughput as shown in the governing equation regression below.  The other main factor in determining the people is the complexity of the development.  Two measure of complexity include water depth (deeper is more complex operating environment) and production efficiency (higher production efficiency is more difficult and requires more resources). 
 
Below we have a summary of the figures of merit, governing equation as derived by the relationships noted above, and a graph of the predicted values from the governing equation vs. the actual results.
 
[[File:FOM rev4.png|1000px|frameless]]


Besides defining what the FOMs are, this section of the roadmap should also contain the FOM trends over time dFOM/dt as well as some of the key governing equations that underpin the technology. These governing equations can be derived from physics (or chemistry, biology ..) or they can be empirically derived from a multivariate regression model. The table below shows an example of a key governing equation governing (solar-) electric aircraft.
[[File:POBpred.png|1000px|frameless]]


[[File:Section 4_2.JPG]]
When we look at the the changes in time for people vs. capacity, we do not see not see any trend.  This measure has not been a key consideration in the past, and we would consider it a disruptive technology as it changes the focus of figures of merit.
[[File:PeoplevCapacity.png|1000px|frameless]]


==Alignment with Company Strategic Drivers==
==Alignment with Company Strategic Drivers==
The table below shows an example of potential strategic drivers and alignment of the 2SEA technology roadmap with it.


[[File:Section 5.JPG]]
The table below outlines potential strategic drivers and statements of alignment of remote operated platforms with the company targets.  These targets are all inline with both Company and joint industry forecasts.  


The list of drivers shows that the company views HAPS as a potential new business and wants to develop it as a commercially viable (for profit) business (1). In order to do so, the technology roadmap performs some analysis - using the governing equations in the previous section - and formulates a set of FOM targets that state that such a UAV needs to achieve an endurance of 500 days (as opposed to the world record 26 days that was demonstrated in 2018) and should be able to carry a payload of 10 kg. The roadmap confirms that it is aligned with this driver. This means that the analysis, technology targets, and R&D projects contained in the roadmap (and hopefully funded by the R&D budget) support the strategic ambition stated by driver 1. The second driver, however, which is to use the HAPS program as a platform for developing an autonomy stack for both UAVs and satellites, is not currently aligned with the roadmap.
[[File:Stratdriver2.jpg|600px|frameless]]


==Positioning of Company vs. Competition==
==Positioning of Company vs. Competition==
The figure below shows a summary of other electric and solar-electric aircraft from public data.


[[File:Section 6.JPG]]
The process capability listed in the competition table is illustrative of either 1-no separation (process fluid transportation), 2-stage (liquid/gas) or 3-stage oil/water/gas. As illustrated in the competition landscape, there is one known unmanned wellhead platform that is operated remotely from a central facility and many offshore processing platforms in operation. 


The aerobatic aircraft Extra 330LE by Siemens currently has the world record for the most powerful flight certified electric motor (260kW). The Pipistrel Alpha Electro is a small electric training aircraft which is not solar powered, but is in serial production. The Zephyr 7 is the previous version of Zephyr which established the prior endurance world record for solar-electric aircraft (14 days) in 2010. The Solar Impulse 2 was a single-piloted solar-powered aircraft that circumnavigated the globe in 2015-2016 in 17 stages, the longest being the one from Japan to Hawaii (118 hours).  
[[File:Compe.jpg|600px|frameless]]


SolarEagle  and Solara 50 were both very ambitious projects that aimed to launch solar-electric aircraft with very aggressive targets (endurace up to 5 years) and payloads up to 450 kg. Both of these projects were canceled prematurely. Why is that?
Note that there are many examples (excluded from table, included in Pareto Front) with 3-stage process capability at various capacities and POB values.  This table was compiled to show an estimate of a future installation of a simple processing platform with 2-stage separation operated remotely.


[[File:Section 6_2.JPG]]
==Technical Model==


The Pareto Front (see Chapter 5, Figure 5-20 for a definition) shown in black in the lower left corner of the graph shows the best tradeoff between endurance and payload for actually achieved electric flights by 2017. The Airbus Zephyr, Solar Impulse 2 and Pipistrel Alpha Electro all have flight records that anchor their position on this FOM chart. It is interesting to note that Solar Impulse 2 overheated its battery pack during its longest leg in 2015-2016 and therefore pushed the limits of battery technology available at that timeWe can now see that both Solar Eagle in the upper right and Solara 50 were chasing FOM targets that were unachievable with the technology available at that time. The progression of the Pareto front shown in red corresponds to what might be a realistic Pareto Front progression by 2020. Airbus Zephyr Next Generation (NG) has already shown with its world record (624 hours endurance) that the upper left target (low payload mass - about 5-10 kg and high endurance of 600+ hours) is feasible. There are currently no plans for a Solar Impulse 3which could be a non-stop solar-electric circumnavigation with one pilot (and an autonomous co-pilot) which would require a non-stop flight of about 450 hours. A next generation E-Fan aircraft with an endurance of about 2.5 hours (all electric) also seems within reach for 2020. Then in green we set a potentially more ambitious target Pareto Front for 2030. This is the ambition of the 2SEA technology roadmap as expressed by strategic driver 1. We see that in the upper left the Solara 50 project which was started by Titan Aerospace, then acquired by Google, then cancelled, and which ran from about 2013-2017 had the right targets for about a 2030 Entry-into-Service (EIS), not for 2020 or sooner. The target set by Solar Eagle was even more utopian and may not be achievable before 2050 according to the 2SEA roadmap.
The approach was to focus on the value drivers of the capital and operating cost per barrel as predictable and measurable outcomes. The safety benefits are much harder to realize without very in-depth models of risk factorsThe methodology identified that there are key environmental attributes accounted for in the water depth and throughput as well as the reliability component. The People on Board is a proxy for the operating and maintenance staff which keep the production online. From a cost component, there is infrastructure and ongoing operating expense to support. Sensitivities were calculated through individual and multiple-regression of projects with the key parameters.  The reference offshore facility of 75 MBOEPD and 100 people was then used to assess the percentage impact of a one percent change in each key variable. An average variation was used to calculate the sensitivity using the sample data. Note, in all cases the coefficients demonstrate significant uncertainty (t scores typically between 1 and 2). In the design People on Board, we were able to use actual data. For the Opex and Capex sensitivities, we need to use a combination of sources. External data available through WoodMac was utilized to identify cost sensitivity to water depth and throughput.  Proprietary studies were used to estimate the sensitivity to design people on board. These studies included model forecasts of capital and operating cost changes with change in People on Board.  Simple ratios with the capacity could be used to get the impact of People on Board to capital and operating cost per barrel.  While we believe the results are representative, we do note that there is significant uncertainty. <br><br>


==Technical Model==
'''Equations''' <br>
In order to assess the feasibility of technical (and financial) targets at the level of the 2SEA roadmap it is necessary to develop a technical model. The purpose of such a model is to explore the design tradespace and establish what are the active constraints in the system. The first step can be to establish a morphological matrix that shows the main technology selection alternatives that exist at the first level of decomposition, see the figure below.
People on Board = 272 * Production Efficiency + 0.64 * Throughput (MBOEPD) + 0.02 * Water Depth (m) - 240 [source: 11 real projects, confidential data] <br>
Opex per Barrel = 0.001 * Water Depth (m) - 0.004 * Throughput (MBOEPD) + f(People on Board) + 11.1 [source: 389 offshore projects from WoodMac] <br>
Capex per Barrel = 0.0004 * Water Depth (m) - 0.012 * Throughput (MBOEPD) + f(People on Board) + 16.2 [source: 387 offshore projects from WoodMac] <br>
Note: sensitivity of Opex and Capex per barrel to People on Board coefficient and data confidential and withheld. <br><br>


[[File:Section 7_.JPG]]
'''Key Points''' <br>
The key insight is that the People on Board FOM is most sensitive to production efficiency which is a proxy for reliability.  This intuitively makes sense as the people are on the platform to ensure that operations are stable and continuous.  It also highlights that by designing to a higher reliability, one can tradeoff the number of people on the platform.  This dovetails to Capex / bbl and Opex / bbl metrics.  The hypothesis is that there is a tradeoff between Capex, Opex, and people.  Interestingly enough, reducing the design People on Board reduces both Capex and Opex.  The intuition is that it avoids capital expenditure such as living quarters and other systems designed specifically for people.  Having less people means a lot less associated expenses which is logical.  This brings up the interesting point that if it reduces capex and opex per barrel, then why have there been no discernible changes is People on Board over time?  The answer is not clear at this time but might be related to the increased technical complexity of developments over time (easier developments are no longer available) and the technical challenges with increasing reliability / decreasing people.  Of note, it is also possible that the relationship is not linear and while completely removing people is highly economic, it might take incremental steps that have marginal economics.


It is interesting to note that the architecture and technology selections for the three aircraft (Zephyr, Solar Impulse 2 and E-Fan 2.0) are quite different. While Zephyr uses lithium-sulfur batteries, the other two use the more conventional lithium-ion batteries. Solar Impulse uses the less efficient (but more affordable) single cell silicon-based PV, while Zephyr uses specially manufactured thin film multi-junction cells and so forth.
[[File:FOMtornados.jpg|1200px|frameless]]<br><br>
<br>


The technical model centers on the E-range and E-endurance equations and compares different aircraft sizing (e.g. wing span, engine power, battery capacity) taking into account aerodynamics, weights and balance, the performance of the aircraft and also its manufacturing cost. It is important to use Multidisciplinary Design Optimization (MDO) when selecting and sizing technologies in order to get the most out of them and compare them fairly (see below).
[[File:ROPmorphmat2.jpg|1200px|frameless]]


[[File:Section 7_2.JPG]]
==Financial Model==
The value of the technology was modeled in two different evaluations: 1.) remote operated platform with no technology improvements vs. and 2.) remote operated platform with technology improvements vs. current standard architecture.  This allows the problem to be separated into the decision component and technical challenges.  This required three different cases for the economics.  First, we ran the constructed a baseline model with the key assumptions of 100 People on Board (POB) design capacity and 75 MBOEPD to reflect current developments.  Next, we constructed the performance if the POB was reduced to zero using current technology for the 100% reduction in POB - Current Technology shown in the graph below.  We utilized the results of the tornado analysis to forecast the impact on reliability and capital cost.  Finally, we constructed the 100% reduction in POB - New Technology by implementing R&D / R&T projects to improve reliability.  These projects are estimated at an R&D cost of $160MM over 4 years to yield an estimated value of nearly $800MM NPV.  The results are shown in both absolute and delta terms below. <br><br>
The results indicate the technology reduces the investment required, so we first considered the rate of return as a measure of capital efficiency in addition to NPV.  As can be seen below, reducing the people operating a platform is essentially a net neutral financially.  The NPV decreases due to lower revenues from reduced reliability, but the rate of return remains essentially the same due to an offsetting reduction in capex.  These can be seen in the delta cash flow from baseline charts below as well.  The key to the viability of the technology is the success case which demonstrates large increases in both rate or return as well as NPV by implementing projects to improve the reliability of the remotely operated facility.


==Financial Model==
[[File:EconomicsRev2.jpg|600px|frameless]]
The figure below contains a sample NPV analysis underlying the 2SEA roadmap. It shows the non-recurring cost (NRC) of the product development project (PDP), which includes the R&D expenditures as negative numbers. A ramp up-period of  4 years is planned with a flat revenue plateau (of 400 million per year) and a total program duration of 24 years.


[[File:Section 8.JPG]]
[[File:BaselineCashFlow.jpg|600px|frameless]]
[[File:NTCF rev2.jpg|600px|frameless]]


==List of R&T Projects and Prototypes==
==List of R&T Projects and Prototypes==
In order to select and prioritize R&D (R&T) projects we recommend using the technical and financial models developed as part of the roadmap to rank-order projects based on an objective set of criteria and analysis. The figure below illustrates how technical models are used to make technology project selections, e.g based on the previously stated 2030 target performance and Figure 8-17 (see the Chapter 8 of the text) shows the outcome if none of the three potential projects are selected.
We identified several projects and design choices to bridge the reliability gap in current technology created from remote operation.  Starting with the reliability equation, we aim to reduce the probability of failure in a year as well as the mean time to repair (MTTR, fraction of a year).
Reliability = 1 -Σ(Prob_failure*MTTR)
These projects are summarized in the table below and organized in decreasing impact:
 
[[File:RTprojects.png|600px|frameless]]
 
The R&T projects identified each require a combination of enabling technologies on an integrated platform to ensure success.  As currently estimated the vast majority of the spend is investment in software and systems.  There are hardware components, but these are estimated to be rather small compared to the investment in the software. The figure below shows the enabling Technologies required to deliver projects #3&5 - Central Control Center and Advanced Remote Sensing which are targeted at achieving data-analytics driven  predictive maintenance of the facility thus minimizing/eliminating frequent human intervention for maintenance and ensuring high platform reliability. The key enabling Technologies target improved sensing capability, Equipment data acquisition/aggregation, advanced data analytics and high-reliability data transmission infrastructure which build into the upfront cost of Technology implementation.
 
[[File:Predictive Maintenance Enablers.png|800px|frameless]]
 
The resulting stacking of these five projects results in an estimated improvement of 29% in reliability as seen below in the waterfall charts showing both the change in reliability and associated value.  It is clear that the projects through the Central Control Center are critical to achieving the objectives profitably. The robotics and remote sensing carry extra risk and high infrastructure costs and are not necessary to achieve the objectives.  They appear to add value, but are not core.
 
[[File:Rwaterfall.png|400px|frameless]]
 
[[File:ReliabilityValueWF.png|400px|frameless]]
 
We can then show the result on the Pareto Chart indicating the new efficient frontier point to 0 Design POB, 75 MMBOEPD capacity. The chart shows that 2 different Pareto frontiers exist today for Oil and Gas Platforms depending on the Operating water depth - Deepwater versus Shallow Water Depth. The Shallow water Pareto is shown in the blue line while the Deepwater Pareto shifts to the right of the Blue curve driven by the increased need for People per Throughput driven by the complexity of operations and distance to other support platform. Our target by 2025 is to push the Deepwater Platform Pareto Frontier to the left by sanctioning a project targeting '0' POB for a medium sized platform with about 75MBPOED Throughput.  Typically, developments of this size take ~3 years to start production.


[[File:Section 9.JPG]]
[[File:Pareto2.png|1200px|frameless]]
Note, projects not labeled Chevron are industry competitors.
<sup>7</sup>https://www2.deloitte.com/content/dam/Deloitte/us/Documents/process-and-operations/us-cons-predictive-maintenance.pdf<br><br>
 
<br>
<br>
Mapping out our key implementation projects, development of the Digital Asset Lifecycle Management Platform (operational backbone) is the baseline project for deployment.  Current state is this is handled at a project-specific level, for which requirements are provided from the Business Unit of operation.  The shift to a Company-wide list of requirements is a precursor that is listed as third-party owned.  From this perspective, we will rely on third-party experts to aid in development and deployment of the requirements to ensure full integration for all applications.  The central control centers currently in operation are solely for monitor and optimization projects, typically without real-time data.  The operational backbone will provide real-time information allowing full remote-control of facilities, including both operational and maintenance control.  Use of robotics in the form of drone surveillance is current practice and will continue to expand. Building from the remote control centers, robotics control is aimed for deployment in 2025 via third-party.  Note, we do not recommend the advanced remote sensing noted earlier as the value to reliability and NPV is small.  Finally, looking beyond solely remote operation of processing facilities, multi-discipline integration of real-time data from the reservoir to product is the map for success. 
 
[[File:Finfinarrow.jpg|1000px|frameless]]


==Key Publications, Presentations and Patents==
==Key Publications, Presentations and Patents==
A good technology roadmap should contain a comprehensive list of publications, presentations and key patents as shown in Figure 8-19. This includes literature trends, papers published at key conferences and in the trade literature and trade press.
[[File:Rmpp.jpg|600px|frameless]]


[[File:Section 10 1.JPG]]
==Technology Strategy Statement==
Our target is to develop a remote operated offshore oil and gas platform in the Gulf of Mexico to be sanctioned (start detailed engineering) by 2025.  Currently these large developments have ~100-150 people living on the platform.  To reduce the safety risk, operational expense, and capital infrastructure to support people living on the platform we are pursuing a series of technology projects.  As incremental goals, we will implement a series of project aimed at improving both automation and reliability.  These two aspects are critical to economically remove people from offshore facilities.  First, improving the digital infrastructure is critical as a primary investment to enable the reliability projects.  Next, implementation of the Central Control Center will allow for improved data and predictive maintenance to improve reliability.  Finally, robotic intervention will reduce mean-time-to-repair to decrease the downtime associated with an unplanned event.  Advanced remote sensing was not included in the selected projects as the reliability and NPV value are estimated to be negligible compared to the overall project.  Noting the financials, the projects through the Central Control Center are critical to achieving the goal of remote operation economically.  The additional projects have higher risk and lower value and should be further assess for value.  They are not critical at this time.


==Technology Strategy Statement==
A technology roadmap should conclude and be summarized by both a written statement that summarizes the technology strategy coming out of the roadmap as well as a graphic that shows the key R&D investments, targets and a vision for this technology (and associated product or service) over time. For the 2SEA roadmap the statement could read as follows:


'''Our target is to develop a new solar-powered and electrically-driven UAV as a HAPS service platform with an Entry-into-Service date of 2030. To achieve the target of an endurance of 500 days and useful payload of 10 kg we will invest in two R&D projects. The first is a flight demonstrator with a first flight by 2027 to demonstrate a full-year aloft (365 days) at an equatorial latitude with a payload of 10 kg. The second project is an accelerated development of Li-S batteries with our partner XYZ with a target lifetime performance of 500 charge-discharge cycles by 2027. This is an enabling technology to reach our 2030 technical and business targets.'''
[[File:Final arrow rop.jpg|800px|frameless]]

Latest revision as of 01:42, 11 December 2019

Technology Roadmap Sections and Deliverables

On an offshore oil and gas platform, operators are physically located on the facility to provide local intervention with a goal of ensuring safe, reliable processing capabilities. Introduction of a remote operated platform removes operations and maintenance personnel from being physically located on the platform to a central operating location where the focus is on real-time monitoring and intervention is achieved. It is worth noting that this is primary a systems integration and scale-up issue. The vast majority of the underlying technology has been proven across different applications, but never integrated into a new large deepwater development.

The unique identifier outlined in the roadmap is that of a remote operated oil and gas processing platform, level 5 - ROPP5

Roadmap Overview

The high-level working principle and architecture of a remote operated oil and gas processing platform is depicted in the below. The core infrastructure of any offshore oil and gas processing facility consists of an inlet well fluid stream and initial separation followed by oil, water, and gas processing streams. Utilities support all processing streams and include main power, instrument air, heating and cooling medium, fresh and seawater supply to name a few.

RdmpOverview.jpg

As of today, these processes are more or less controlled via central control room on a production facility with manual intervention by operations and maintenance personnel, as required. The elimination of onsite personnel is depicted in our overview to highlight a remote control center operated via cloud computing technology.

Design Structure Matrix (DSM) Allocation

We can extract from the DSM below that the remote operated processing platform is composed of and dependent on a series of decisions and technologies. These decisions and subsequent technologies include facility location, communication and control method, process design, monitoring and intervention.

Remote Operated Platform DSM

Level 1: Communication and structural design, dependent on reservoir location - Platform design: PD1
Level 2: Facility design, dependent on well fluid characteristics - Facility design: FD2
Level 3: Independent, remote monitoring - Remote monitoring design: RMD3
Level 4: Monitoring, dependent on facility operating design - Operating design: SOD4
Level 5: Remote operated platform, dependent on location, well fluid characteristics, and monitoring - Remote Operated Processing Platform: ROPP5

Roadmap Model using OPM

We provide an Object-Process-Diagram (OPD) of the in the figure below. This diagram captures the main object of the roadmap (oil platform), its various processes required for transferring of information to control the facility remotely, as well as the Figures of Merit and main processes.


OPD Unmanned Rev1.jpg

An Object-Process-Language (OPL) description of the roadmap scope is auto-generated via Opcloud and provided below. This is simply an export of the OPD in a formal natural language.

OPL Rev 3.jpg


Figures of Merit

The table below show a list of FOMs by which a remote operated processing platform can be assessed. The best indicator of the profitability of any oil and gas development is the unit cost, which is the capital required to produce the barrels of oil in the development and the unit operating expense. This is a strong function of the People on Board, but the information is highly confidential and cannot be shared publicly. It is shown as f(People on Board) in the equations to maintain confidentiality. Any sustaining technology will need to be competitive within the capital and operating cost per barrel before other considerations. The main consideration outside of cost is safety and environment. Focusing on safety as measured through injuries per 1000 hrs worked, the simplest way to reduce the safety incidents is to remove the people. Removing people is the focus of this technology. Our measure is Design People on Board (POB) per throughput to normalized for the size of the development. When plotted with historical data we see a clear trend with throughput as shown in the governing equation regression below. The other main factor in determining the people is the complexity of the development. Two measure of complexity include water depth (deeper is more complex operating environment) and production efficiency (higher production efficiency is more difficult and requires more resources).

Below we have a summary of the figures of merit, governing equation as derived by the relationships noted above, and a graph of the predicted values from the governing equation vs. the actual results.

FOM rev4.png

POBpred.png

When we look at the the changes in time for people vs. capacity, we do not see not see any trend. This measure has not been a key consideration in the past, and we would consider it a disruptive technology as it changes the focus of figures of merit.

PeoplevCapacity.png

Alignment with Company Strategic Drivers

The table below outlines potential strategic drivers and statements of alignment of remote operated platforms with the company targets. These targets are all inline with both Company and joint industry forecasts.

Stratdriver2.jpg

Positioning of Company vs. Competition

The process capability listed in the competition table is illustrative of either 1-no separation (process fluid transportation), 2-stage (liquid/gas) or 3-stage oil/water/gas. As illustrated in the competition landscape, there is one known unmanned wellhead platform that is operated remotely from a central facility and many offshore processing platforms in operation.

Compe.jpg

Note that there are many examples (excluded from table, included in Pareto Front) with 3-stage process capability at various capacities and POB values. This table was compiled to show an estimate of a future installation of a simple processing platform with 2-stage separation operated remotely.

Technical Model

The approach was to focus on the value drivers of the capital and operating cost per barrel as predictable and measurable outcomes. The safety benefits are much harder to realize without very in-depth models of risk factors. The methodology identified that there are key environmental attributes accounted for in the water depth and throughput as well as the reliability component. The People on Board is a proxy for the operating and maintenance staff which keep the production online. From a cost component, there is infrastructure and ongoing operating expense to support. Sensitivities were calculated through individual and multiple-regression of projects with the key parameters. The reference offshore facility of 75 MBOEPD and 100 people was then used to assess the percentage impact of a one percent change in each key variable. An average variation was used to calculate the sensitivity using the sample data. Note, in all cases the coefficients demonstrate significant uncertainty (t scores typically between 1 and 2). In the design People on Board, we were able to use actual data. For the Opex and Capex sensitivities, we need to use a combination of sources. External data available through WoodMac was utilized to identify cost sensitivity to water depth and throughput. Proprietary studies were used to estimate the sensitivity to design people on board. These studies included model forecasts of capital and operating cost changes with change in People on Board. Simple ratios with the capacity could be used to get the impact of People on Board to capital and operating cost per barrel. While we believe the results are representative, we do note that there is significant uncertainty.

Equations
People on Board = 272 * Production Efficiency + 0.64 * Throughput (MBOEPD) + 0.02 * Water Depth (m) - 240 [source: 11 real projects, confidential data]
Opex per Barrel = 0.001 * Water Depth (m) - 0.004 * Throughput (MBOEPD) + f(People on Board) + 11.1 [source: 389 offshore projects from WoodMac]
Capex per Barrel = 0.0004 * Water Depth (m) - 0.012 * Throughput (MBOEPD) + f(People on Board) + 16.2 [source: 387 offshore projects from WoodMac]
Note: sensitivity of Opex and Capex per barrel to People on Board coefficient and data confidential and withheld.

Key Points
The key insight is that the People on Board FOM is most sensitive to production efficiency which is a proxy for reliability. This intuitively makes sense as the people are on the platform to ensure that operations are stable and continuous. It also highlights that by designing to a higher reliability, one can tradeoff the number of people on the platform. This dovetails to Capex / bbl and Opex / bbl metrics. The hypothesis is that there is a tradeoff between Capex, Opex, and people. Interestingly enough, reducing the design People on Board reduces both Capex and Opex. The intuition is that it avoids capital expenditure such as living quarters and other systems designed specifically for people. Having less people means a lot less associated expenses which is logical. This brings up the interesting point that if it reduces capex and opex per barrel, then why have there been no discernible changes is People on Board over time? The answer is not clear at this time but might be related to the increased technical complexity of developments over time (easier developments are no longer available) and the technical challenges with increasing reliability / decreasing people. Of note, it is also possible that the relationship is not linear and while completely removing people is highly economic, it might take incremental steps that have marginal economics.

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Financial Model

The value of the technology was modeled in two different evaluations: 1.) remote operated platform with no technology improvements vs. and 2.) remote operated platform with technology improvements vs. current standard architecture. This allows the problem to be separated into the decision component and technical challenges. This required three different cases for the economics. First, we ran the constructed a baseline model with the key assumptions of 100 People on Board (POB) design capacity and 75 MBOEPD to reflect current developments. Next, we constructed the performance if the POB was reduced to zero using current technology for the 100% reduction in POB - Current Technology shown in the graph below. We utilized the results of the tornado analysis to forecast the impact on reliability and capital cost. Finally, we constructed the 100% reduction in POB - New Technology by implementing R&D / R&T projects to improve reliability. These projects are estimated at an R&D cost of $160MM over 4 years to yield an estimated value of nearly $800MM NPV. The results are shown in both absolute and delta terms below.

The results indicate the technology reduces the investment required, so we first considered the rate of return as a measure of capital efficiency in addition to NPV. As can be seen below, reducing the people operating a platform is essentially a net neutral financially. The NPV decreases due to lower revenues from reduced reliability, but the rate of return remains essentially the same due to an offsetting reduction in capex. These can be seen in the delta cash flow from baseline charts below as well. The key to the viability of the technology is the success case which demonstrates large increases in both rate or return as well as NPV by implementing projects to improve the reliability of the remotely operated facility.

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List of R&T Projects and Prototypes

We identified several projects and design choices to bridge the reliability gap in current technology created from remote operation. Starting with the reliability equation, we aim to reduce the probability of failure in a year as well as the mean time to repair (MTTR, fraction of a year). Reliability = 1 -Σ(Prob_failure*MTTR) These projects are summarized in the table below and organized in decreasing impact:

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The R&T projects identified each require a combination of enabling technologies on an integrated platform to ensure success. As currently estimated the vast majority of the spend is investment in software and systems. There are hardware components, but these are estimated to be rather small compared to the investment in the software. The figure below shows the enabling Technologies required to deliver projects #3&5 - Central Control Center and Advanced Remote Sensing which are targeted at achieving data-analytics driven predictive maintenance of the facility thus minimizing/eliminating frequent human intervention for maintenance and ensuring high platform reliability. The key enabling Technologies target improved sensing capability, Equipment data acquisition/aggregation, advanced data analytics and high-reliability data transmission infrastructure which build into the upfront cost of Technology implementation.

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The resulting stacking of these five projects results in an estimated improvement of 29% in reliability as seen below in the waterfall charts showing both the change in reliability and associated value. It is clear that the projects through the Central Control Center are critical to achieving the objectives profitably. The robotics and remote sensing carry extra risk and high infrastructure costs and are not necessary to achieve the objectives. They appear to add value, but are not core.

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We can then show the result on the Pareto Chart indicating the new efficient frontier point to 0 Design POB, 75 MMBOEPD capacity. The chart shows that 2 different Pareto frontiers exist today for Oil and Gas Platforms depending on the Operating water depth - Deepwater versus Shallow Water Depth. The Shallow water Pareto is shown in the blue line while the Deepwater Pareto shifts to the right of the Blue curve driven by the increased need for People per Throughput driven by the complexity of operations and distance to other support platform. Our target by 2025 is to push the Deepwater Platform Pareto Frontier to the left by sanctioning a project targeting '0' POB for a medium sized platform with about 75MBPOED Throughput. Typically, developments of this size take ~3 years to start production.

Pareto2.png Note, projects not labeled Chevron are industry competitors. 7https://www2.deloitte.com/content/dam/Deloitte/us/Documents/process-and-operations/us-cons-predictive-maintenance.pdf



Mapping out our key implementation projects, development of the Digital Asset Lifecycle Management Platform (operational backbone) is the baseline project for deployment. Current state is this is handled at a project-specific level, for which requirements are provided from the Business Unit of operation. The shift to a Company-wide list of requirements is a precursor that is listed as third-party owned. From this perspective, we will rely on third-party experts to aid in development and deployment of the requirements to ensure full integration for all applications. The central control centers currently in operation are solely for monitor and optimization projects, typically without real-time data. The operational backbone will provide real-time information allowing full remote-control of facilities, including both operational and maintenance control. Use of robotics in the form of drone surveillance is current practice and will continue to expand. Building from the remote control centers, robotics control is aimed for deployment in 2025 via third-party. Note, we do not recommend the advanced remote sensing noted earlier as the value to reliability and NPV is small. Finally, looking beyond solely remote operation of processing facilities, multi-discipline integration of real-time data from the reservoir to product is the map for success.

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Key Publications, Presentations and Patents

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Technology Strategy Statement

Our target is to develop a remote operated offshore oil and gas platform in the Gulf of Mexico to be sanctioned (start detailed engineering) by 2025. Currently these large developments have ~100-150 people living on the platform. To reduce the safety risk, operational expense, and capital infrastructure to support people living on the platform we are pursuing a series of technology projects. As incremental goals, we will implement a series of project aimed at improving both automation and reliability. These two aspects are critical to economically remove people from offshore facilities. First, improving the digital infrastructure is critical as a primary investment to enable the reliability projects. Next, implementation of the Central Control Center will allow for improved data and predictive maintenance to improve reliability. Finally, robotic intervention will reduce mean-time-to-repair to decrease the downtime associated with an unplanned event. Advanced remote sensing was not included in the selected projects as the reliability and NPV value are estimated to be negligible compared to the overall project. Noting the financials, the projects through the Central Control Center are critical to achieving the goal of remote operation economically. The additional projects have higher risk and lower value and should be further assess for value. They are not critical at this time.


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