The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
- Data Science & Engineering Analytics
The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
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Abstract With the emerging necessity of carbon capture and storage (CCS), many companies are evaluating the possibilities of CCS implementation in their assets. Technical evaluation for converting existing fields to CCS projects includes various topics such as carbon dioxide (CO2) transportation and its economics among other topics. Selecting a method for CO2 transportation becomes important when the target site is distant from the CO2 source, particularly if located offshore. The Intergovernmental Panel on Climate Change (IPCC) special report on CCS has identified that a liquefied CO2 (LCO2) carrier would be the lowest-cost option for distances more than 1700 km. An LCO2 carrier can also be the best option when transporting CO2 abroad to benefit from the international carbon tax, which has been collecting global interest. Along with this increased interest in LCO2 carriers, shipbuilding and engineering companies are developing their ships. When an LCO2 carrier is used for offshore CCS, the ship would be located right above the target site to minimize the length of pipelines. As this distance between the LCO2 carrier and the target reservoir is shorter than other transportation options, the traditional modeling approach uses a standalone model of the LCO2 carrier. This approach excludes pipeline models when estimating required operating conditions of the carrier assuming a fixed outlet boundary condition. However, this boundary condition may differ from the actual value. Furthermore, in real systems, operating conditions (i.e., pressure and temperature) are not constant over time. Ignoring the dynamic interaction with downstream pipelines may lead to subsequent differences in simulation results. The actual thermo-hydraulics behavior of LCO2 carrier cannot be reproduced when standalone models are introduced. In this study, a standalone LCO2 carrier model and an integrated dynamic CCS model connecting the LCO2 carrier, injection equipment, riser, pipeline, and wellbore were developed. The standalone LCO2 carrier model predicts the behavior of a whole ship from two LCO2 tanks to the carrier's outlet, which would be connected to the riser of the CO2 injection system. The integrated model calculates the whole CO2 injection system from two LCO2 tanks to the target reservoir by linking the standalone LCO2 carrier model and a flow model starting from the riser to the injection wellbore. The simulation results showed that the required CO2 pump discharge pressure of the integrated model was 5 bar higher than the standalone model to meet the target flow rate. As the required discharge pressure increased, the average speed and power consumption of the CO2 pump increased by 2.5% and 7%, respectively. In this comparison study we demonstrated that the integrated model could accurately represent the overall system behavior. No risk of solid CO2 formation was identified during unloading of two LCO2 tanks. By using the developed integrated model, three different case studies were conducted to analyze the effect of rigorous heat transfer in LCO2 tanks, simultaneous tank unloading, and initial startup operation on the thermal-hydraulic performance of the system, respectively. The first case demonstrated that modeling the tanks with high-thickness thermal insulation is close to an adiabatic condition. The required discharge pressure of the CO2 pump was the same, and the final pressure and temperature of the tank holdup increased by 1 bar and 2°C, respectively. The second case showed that changing the operation from sequential to simultaneous unloading of the two LCO2 tanks removed the disturbances observed during the transition of tanks in the sequential case. This removes potential instabilities in the pump controller and avoids any impact on the injection system performance. The unloading time was only 20 seconds shorter, and the required pump discharge pressure was the same. The third case demonstrated that the integrated model could analyze the initial startup operation, which displaces nitrogen (N2) and methane (CH4) in the pipelines and wellbore with CO2, which standalone models cannot predict. It took 500 seconds to fully displace N2 and CH4 in the system with CO2. Furthermore, the required valve opening time (19 seconds after injection commences) to prevent backflow from the reservoir could be determined. In conclusion, dynamically integrated modeling can help identify interactions that are not apparent in the traditional standalone modeling approach. The integrated model can evaluate system behavior and possible operational risks that cannot be observed in standalone models. Simulation results in this work demonstrated that the dynamically integrated CCS model captures more realistic behavior of the whole CO2 injection system to help optimize the design and operation of a CCS project. Developing a plan to address these interactions through the integrated dynamic simulation can result in a more stable operation.
This new webinar series provides comprehensive information about the Digital Oilfield (DO), from it's history to current applications. This package gives you access to all three webinars in the series or you can click to register for each individually. Each webinar is a 1-hour presentation followed by a 30-minute Q&A session. This webinar is categorized under the Data Science and Engineering Analytics discipline. Session I - What Have we Learned in the First 15 Years of Digital Oilfield?
Karam Al Yateem, SPE, is manager of the Production Engineering Technical Support Division with Saudi Aramco. His current responsibilities cover production; intelligent fields and infrastructure; operations; health, safety, and environment; and asset integrity, spanning onshore and offshore oil, gas, and water wells and producing fields and facilities. Al Yateem has been author or coauthor of more than 35 technical papers and is an active SPE member and the recipient of various awards, including the SPE International Young Member Outstanding Services Award. He is certified as both an SPE Certified Professional and a Project Management Professional. Al Yateem holds a bachelor's degree in petroleum engineering from King Fahd University of Petroleum and Minerals; a master's degree in petroleum engineering, specializing in smart oilfield technologies and management, from the University of Southern California; and an executive MBA degree from Prince Mohammad Bin Fahd University.
Montana Technological University was the recipient of a donation from Petroleum Experts (Petex) in the form of educational licenses for their geological and petroleum engineering software, valued at approximately 6.5 million. Geological, petroleum, and civil engineering students enrolled in structural geology for engineers and sedimentology and petroleum geology courses at the university will have the opportunity to use the state-of-the-art technology. Petroleum Experts, a Scotland-based company created in 1990, offers a variety of products including integrated production modeling software, digital oilfield software, and MOVE software which specializes in structural modeling and analysis. "We are very grateful to Petex for offering this impressive donation, and we are positive this will help us train future engineering leaders in the geological and petroleum engineering fields," Department Head of Geological Engineering Glenn Shaw shared in a press release. Both undergraduate and graduate students in Montana Tech's 70-year-old petroleum engineering department will have the opportunity to use the software.
Rosa, Andrea (Eni NR) | Wiegand, Klaus (Stone Ridge Technology) | Mukundakrishnan, Karthik (Stone Ridge Technology) | Pizzolato, Alberto (Eni NR) | Panfili, Paola (Eni NR) | Cominelli, Alberto (Eni NR) | Picone, Silvia (Eni NR) | Ruffino, Rosario (Eni NR) | Patacchini, Leonardo (Stone Ridge Technology)
Abstract We present the development of a field-scale simulation tool coupling the model of Zohr, a super giant deepwater gas reservoir requiring a multi-million active cells grid with dual porosity/dual permeability formulation, to its gathering network. Deepwater field development often relies on a complex subsea gathering infrastructure, possibly evolving over time and leading to a complex topology. Here, facilities route the multiphase production stream to an onshore compression plant throughout a network with bifurcations. To perform integrated modeling of such assets, we have developed a Facility Network Solver (FNS) with flexible topology, whose formulation relies on a graph representation with continuity equations at nodes and tabulated constitutive equations at edges. FNS was designed to be integrated with the industrial grade GPU-based reservoir simulator used in the company, both jointly developed by Eni and Stone Ridge Technology, with emphasis on preserving usability and simulation performance. The correctness of FNS results in the presence of bifurcations was thoroughly assessed in standalone mode, through benchmarking against what is today considered a reference commercial network solver. FNS integration with the reservoir simulator enables forecasts where the back-pressure is taken into account. Engineers can better assess the viability of different development scenarios, including dynamic upgrades to the network topology, using reservoir simulation workflows they are well accustomed to. In particular, a single tool replaces often heterogeneous associations of third-party software, without impacting simulation time. Sensitivity analyses were performed on both coupling frequency and location. It was concluded that periodic coupling at the well-head was a satisfactory setting, yielding negligible performance overhead with respect to standalone reservoir simulations, thus enabling the integrated model to be used routinely as the sole simulation model.
A digital twin is essentially a digital representation of a physical system such as a well, pump, compressor, or a series of connected items. Sometimes, machine-learning algorithms can assist in analyzing large amounts of data within domains such as preventive maintenance. The value proposition of a digital twin is to have a complete overview of all fluid streams in the production and injection network to enable automation of production capacity planning subject to current and future constraints. The digital solution must be versatile, maintainable, accurate, and with a quick turnaround time to address dynamic changes in market demand as well as the supply side down to the individual wells. Integrated asset models (IAM) have been around for the past two decades or so.
Abstract Pore-to-process solutions using the integrated asset modeling approach have been successfully developed and implemented to unlock the ultimate real values of a complex gas-condensate light-oil asset in the Middle East. The studied asset was complex, involving multiple reservoirs with dual porosity that required expertise from different domains from subsurface to surface facilities to collaborate and fully understand the complexity of fluid properties, geological properties, network backpressures, and operational constraints of the existing process facilities to recognize any opportunities of optimizing the current field development plan, thereby unlocking the true value of the studied asset. Integrated asset modeling was adopted for pore-to-process solutions that enable better management of asset operation and identification of optimal field development strategy, where seven black-oil subsurface reservoir models were integrated to six compositional wells, pipelines networks, and subsequently to six process facility models. Black-oil delumping and compositional fluid delumping were implemented to ensure fluid fidelity from pore to process. This approach established the fundamentals of effective modeling solutions that enabled multiple interdependent models to be integrated into a single production model while preserving the fidelity of individual models to reduce uncertainties and to increase confidence in the simulation results with consideration of various component model interactions, system constraints, and backpressure effects. The integrated subsurface reservoir models, compositional surface network, and process facility models brought insights and a better understanding of flow assurance, well, and pipeline integrity issues that may arise. Simulation results displayed on the asset overview dashboard helped to validate production operation strategy and suitability of the process designs. The pore-to-process solutions empowered comprehensive assessment of various field development plans to minimize uncertainties, mitigate risks; and optimize the overall production performance and reservoir recovery via an evergreen integrated model with the ability to account for the complete system constraints, interactions, and backpressure effects between various models in one integrated simulation platform. Integration of subsurface to process facility models have assisted in determining the optimum distribution of fluids per facility and surveillance of facilities performance. This project has delivered some key asset-level decisions; for example, the possibility of increased recoverable reserves by making changes to the existing process equipment (capacity and/or operational), and flow paths of producing wells, in addition to improved forecast accuracy, capital expenditure prediction, and optimal operational efficiency. The developed pore-to-process solutions confirmed that the integrated asset modeling approach could effectively validate various field development strategies with consideration of anticipated rig events, required equipment changes (both well completion and process facilities), and subsequently to identify the opportunities of increased production and ensuring safe asset operation. By keeping the integrated asset models live and evergreen, the availability and accessibility of these models (pore to process) established the main pillars of a digital oil field to unlock the real value of the studied complex asset.
Le Quang, Dat (Hoang Long Hoan Vu Joint Operating Company) | Hoang Ngoc, Dong (Hoang Long Hoan Vu Joint Operating Company) | Rangponsumrit, Manisa (Hoang Long Hoan Vu Joint Operating Company) | Supalasate, Phruettiphan (Hoang Long Hoan Vu Joint Operating Company) | Ngo, Khanh Dong (Hoang Long Hoan Vu Joint Operating Company) | Nguyen, Duy Hung (Hoang Long Hoan Vu Joint Operating Company) | Tran, Minh Dung (PetroVietnam) | Ho, Van Tam (PetroVietnam Exploration Production Corporation) | Luong Van, Chi (SLB) | Ali, Samad (SLB) | Antoneus, Sarjono Tasi (SLB) | Khan, Osama Hasan (SLB) | Hii, Sing Kiet (SLB)
Abstract CNV field in offshore Vietnam is experiencing excessive surface back pressure due to extended production pipeline and increasing field gas-oil ratio (GOR), which not only constraints the production from existing wells but also creates a challenge in evaluating production gain from future development activities. Therefore, it is critical to properly account the back pressure effect to generate a reliable long term production forecast for further investment decision. This paper describes the details of integrating subsurface dynamic reservoir simulation model with surface network simulation model to holistically assess the impact of back pressure. The conventional method of using standalone dynamic simulation model is compared against the integrated model. The well control mode in the reservoir model is updated with the response of the network model, which consist of wells, topside piping, facility equipment and export pipelines. With this approach, the surface pressure constraints and responses will be captured, and the reservoir, well and network performance will be impacted accordingly. A unified field management is designed using an advanced orchestration engine to control the well operating conditions, schedule well activities and activation of equipment in the operational cycle. Thorough assessment can be performed with the inclusion of accounting interactions between reservoir and network parameters. This integrated modelling workflow allows multiple domains of reservoir engineering, production engineering and engineering contractors to collaborate and achieve a better understanding of the impact of surface back pressure by producing a representative forecast of production profile. To address the back pressure problem in the current facility, debottleneck the surface network and improve production was evaluated by installation of additional surface equipments such as booster pump and compressor. In general, the integrated model provides critical insights to the field development planning, evaluation for de-bottle necking surface system and production optimization. There is lack of publication on the successful usage of the integrated surface network with subsurface dynamic simulation as it is uncommon for this feature in conventional modelling workflows. This paper describes the successful case of the implementation of an integrated simulation modelling workflow to simulate long term surface back pressure effect, back pressure from additional production into the system, and benefits of new surface equipment installation. Highly efficient and accurate prediction tool was developed in the scope of this study.
Abstract It's almost certain that the oil & gas industry has passed its plateau for large field discoveries. This places an extra burden on the efficient handling of our mature assets, as reasonable amount of hydrocarbon still exists in such reservoirs. However, the ever-increasing cost of new projects and low production gains hardly justify the economics. This study presents a novel approach applied on a mature gas giant, in order to revitalize old wells, optimize surface network and exploit the scattered sweet spots still prevailing in the field, with integrated surface and subsurface engineering strategies. The field under study has been producing since 1955, with around 112 wells, completed in four independent formations. The primary reservoir (Reservoir-A) is categorized as a depletion-drive gas reservoir and has been on production since the field's inception; the reservoir pressure has depleted from 1,900psi to 300psi. The other formations: Reservoir B, C & D started producing from 1968, 2000 and 2015 respectively. At its peak, the field produced ~1,000 MMscfd gas; but lately, the production decline rose to around 7-8% annually, mainly due to natural depletion. However, deterioration in well performances and limitations of surface facilities (feederlines, Gas-Gathering-Mains (GGMs) and compressors) have also exacerbated this decline, due to the additional pressure drops amassed in their flow dynamics with reservoir pressure depletion & water production. To counter the field's rapid production decline, a comprehensive workflow and an Integrated Asset model was developed, with an absolute focus on the NPV for each development. At the subsurface level, Reservoir-B (still under-developed) was the first targeted, as most of its wells were producing at uneconomical rates. An ant-tracking algorithm was run on the newly acquired 3D-seismic; and natural fractures - near the 02 high producers in Reservoir-B, were analyzed. A workflow was then developed to target similar fractures and the integrated impact, on surface facilities, was evaluated. Finally, three successful pilots were drilled in Reservoir-B and Reservoir-C, to evaluate the post-drill dynamics. Based on the real-time performances of these pilots with existing producers and surface facilities, the integrated field model was updated, coupling the wells, surface facilities and all four reservoirs (with independent reservoir models). As a result of this integrated model, 12 more wells, 09 workovers, 02 GGM optimizations and 03 compressor modification jobs were finalized; giving an overall increase in EUR by 800 BCF, while the NPV of the field increased by 131 MM$. This study offers an innovative approach that has been followed to utilize each data-set systematically, in order to re-vitalize a field even after 84% depletion. The paper also describes the evaluation process for all the optimization opportunities and their impact on the NPVs, to reap the maximum reward from such an old field.
Rubio, Erismar (ADNOC ONSHORE) | Reddicharla, Nagaraju (ADNOC ONSHORE) | Sultan Ali, Mayada Ali (ADNOC ONSHORE) | AL Attar, Mohamed Ali (ADNOC ONSHORE) | Davila, Rayner Samuel (ADNOC ONSHORE) | Kumar, Avnish (ADNOC ONSHORE)
Abstract As hydrocarbon fields are maturing, field sustainable oil production rate (FSOPR) assurance is a challenge for operators to deliver the demands while adhering reservoir management guidelines after accounting for all well & facility downtimes and system inefficiencies. As part of digital transformation journey, ADNOC Onshore has embarked on an initiative to automate FSOPR forecast while eliminating inefficiencies in current process, standardization, leveraging robust data integration and analytics. FSOPR forecast is probably the most complex task performed by oil companies as involved the orchestration of all support and technical departments from the field to the terminal. Effective FSOPR estimation is an integrated effort from reservoir/petroleum engineering, drilling/well services, operations, oil movement, planning, maintenance, engineering and reliability teams. The assurance process requires a structured and integrated approach of data gathering, review, and simulations to produce a reliable forecasts. Before, data/process integration and reporting was time-consuming representing a real challenge as it was managed through manual data-loading in spreadsheets & emails with low visibility to all stakeholders. Now FSOPR is estimated using up-to-date well/network models, including contribution from newly drilled & well reactivation plans, well-performance deterioration and production restoration from field activities. Similarly planned/unplanned losses from reservoir management (RM), field activities & facility maintenance jobs are optimized through the solution, offering integration with reliability models. The automated web-page solution leverages business process engine enabled by intelligent data integration from various workflow elements and allows all stakeholders to collaborate in one portal to have on single version of the truth. The data abstraction layer retrieves information and presented in the solution from legacy systems, production modelling workflows and SAP. The base plan & optimization cases are tracked by process health KPIs and approved using Business process management workflow. FSOPR automation system has been successfully piloted in one ADNOC Onshore assets. This automation was an enabler for better planning and scheduling of maintenance and RM activities and also provides assurance of monthly oil quota achievement by highlighting early potential threats that can hinder the realization of the FSOPR plan, thus corrective action can be taken on time considering best RM practices. The whole process of data gathering, planning, activity scheduling, optimization and approval has been reduced by 60% along with a more rigorous and errorless process. The added value besides enhancing efficiency, it has minimized well downtime and production deferment avoidance of 1-2%. This automation paved the path for FSOPR assurance process standardization across company portfolio of assets. The initiative is aligned with ADNOC digital transformation roadmap to leverage industry 4.0 technologies and digitalization of key upstream business processes in a consistent, integrated and uniform manner. This paper talks about transformation, FSOPR elements, low code solution & integration architecture, analytics, change adoption and benefits realization.