ADNOC has completed the second phase of its ambitious integrated capacity model (ICM) with the overall aim to optimize its fluid production portfolio from the well level to the processing facilities. The business drivers are to establish capabilities to optimize high-value products and proactively react to market demand changes effectively. Such capabilities required a robust thermodynamics engine with component-wise tracking based on a country-wide capacity model network comprised of a myriad of wells, pipelines, and separators. Fluid samples are not available for all the wells in a field. An innovative workflow was created to assign appropriate composition at the well level based on the data set available for a subset of wells. The captured compositions were then passed to the ICM's hydraulic calculation engine to track the fluid compositions at the required nodes across the network. The existing data model was expanded and user interfaces were created to capture the complexities within the network and visualize the changes in fluid properties, particularly composition, density, and flow rates, at the defined nodes. This digital transformation initiative had to overcome the following complexities to improve accuracy and enable faster decision-making: Incorporation of data from more than 20 fields, 150+ reservoirs, 5000+ wells Optimizing the country-wide network model comprised of wells, pipelines, and separators Performing multiple pre-conceived daily scenarios with 60-month forecasts for production and injection rates Accounting for lateral and vertical composition variation within a reservoir Mixing of fluids at different points in the network at different pressures Implementation of a unified equation of state (EOS) to enable component tracking The network model successfully captured this complexity and predicted capacities for all custody-transfer points between upstream and downstream networks demonstrating a good match (>90%) between the actual laboratory-based measurements and the ICM results. The tool also offered the capability to maximize production of desired components at the source level to meet the dynamic energy demands of the country, allowing a 1-3% profit improvement in the base operating plans. Alternate scenarios offer additional views on how to obtain the same upstream liquid production targets while maximizing downstream gas revenues, hence overall country profitability. The ICM recommended suitable targets during crisis conditions to react accurately to unexpected market fluctuations. Implementation of the unified EOS along with component tracking creates new avenues for digital transformation by allowing the operator to optimize high value products and answer to demand changes quickly. Multiple scenarios can be analysed and visualised to support decision makers to increase profitability in a highly competitive hydrocarbon market from rock to stock.

The objective of this paper is to share the results of investigations carried out in the area of separator modelling and performance within the context of ADNOC's Integrated Capacity Model (ICM). Field production from wells is collected via multiple sets of remote degassing stations (RDS) or towers to a centralized separation facility where single or multiple separators allow the separation of the lighter components (contributing to a gas stream) from the heavy components (contributing to the oil stream). We share the investigation results on the following areas related to this separation: Range of pressure and Temperature of 1st stage separators Range of feed (field/asset) compositions & contributing well compositions Effect of separation performance of streams ExclusiveIn presence of other streams Effectiveness of using pseudo k-values in separation performance compared to full EOS separation Use of EOS based approach to production back allocation Comparison of GOR correction methods The primary method used is the performing of simulations with an Equation of State (EOS) model. For the purpose of this study, the single EOS model developed for ADNOC's Integrated Capacity Model (ICM) is used. This model has 15 components and their associated properties. The relative molar content of each component (i.e. the composition of a stream) determines its behavior. The difference in behavior is due to the difference in compositions of streams originating from the various fields/reservoirs/wells. An off-the-shelf software package dedicated to fluid modelling with an EOS is used, with automation, to efficiently execute the EOS computations for thousands of streams. This type of automation is necessary for the purpose of this investigation. The paper depicts the range of compositions from different assets/fields and shows the impact on the range when the streams are aggregated. The paper also shows result of investigation on the efficiency and separation of components in the gas or oil stream if a single well is routed through a separator and when the same well is commingled with other wells. The use of k-values for the separation of streams and their accuracy is also demonstrated in this paper. Novel/Additive Information: A methodological investigation into the performance of separators and the use of k-values in the use of EOS-based production allocation solutions is presented. Secondly, the difference in well stream separation performance when done individually and when in combination with other well streams is computed.

Production planning and performance management imply diverse challenges, mainly when dealing at corporate level in an integrated operating company. Production forecast considers technical capacities, available capacities, and operationally agreed target capacities. Such complex process may hinder taking advantage of market opportunities at the right time. Proactive scenario management and information visibility across the organization are key for success. This paper intends to share the lessons learned while rolling out a countrywide integrated capacity model solution supporting corporate production planning and performance management. The rollout processes aimed at digitizing the monthly and yearly production forecasting. In addition, these processes shall enable formulating proactive scenarios for avoiding shortfalls, maximizing gas throughput, production ramp up, and minimizing operating cost from existing capacity. Abu Dhabi's Integrated Capacity Model is an integrated production planning and optimization system relying on a large-scale subsurface-to-surface integrated asset model system; in this paper, we focus on the incremental progress of the challenges derived from the various rollout efforts. The rollout of such a complex solution relies on basic tenets for managing the change across a large organization. The first tactic is about continuous stakeholder engagement through value demonstration and capabilities building. Engagement is achieved by continuously providing information about proactive shortfall and opportunity identification within the installed asset capacity. Monthly asset reviews provide the basis for user interaction and initiate the basis for establishing ad-hoc production maximization scenarios. Establishing a data governance and performance metrics were also key for embedding the solution in the business processes. The solution delivers tangible and intangible value. From the tangible point of view, it contributes to production efficiency gains by compensating during specific proactively identified shortfalls and after-the-fact events. As a result, our solution has been instrumental in deriving cost reduction scenarios and profitability gains due to optimum GOR management. In addition, the system use has reported various intangible gains in terms of better data utilization, enhanced corporate database quality and reduced overall human load in managing production capacity. The solution described in the paper implements a simpler way the production planning and performance management at corporate level in a large integrated operating company. The in-house developed tool and its implementation is a novel approach in terms of integration, complexity, and practical application to the fields in Abu Dhabi.

Implementing large-scale projects within a company are challenging tasks and often provide a good learning curve that can be beneficial to understand the complexity of the work involved. An integrated subsurface to surface asset modeling solution was implemented at the country level to automate production capacity planning while optimizing shortfall and opportunity identification (Hafez et al., 2018). Several structured business processes support the developed system; it orchestrates the analytical processes followed by the corresponding approval system. A robust data management process was implemented and backed with a business process that includes more than 150 configurable exception rules. Besides, the developed solution leverages the rigor of the first principle and data-driven models to provide a desired and stable outcome ranging from potential evaluation, quota definition, capacity management, business plan validation, and other business processes. The developed solution can isolate wells, sectors, reservoirs, and/or fields for further evaluation. Given the challenge of balancing market demand with profits and subsurface deliverability, a time-efficient, balanced, and integrated solution is expected to provide an edge to an organization in this competitive environment. The Integrated Capacity Model (ICM) system has already been utilized for capacity and deliverability of 2019 and 2020 ADNOC business plans demonstrating 99% agreement with field capacity tests. The system shown +3% profit gains through various production optimization scenarios, while recommending which assets, fields, and/or reservoirs can be targeted to achieve those targets. Developing and implementing the solution at such a large scale surfaced various challenges at organizational, infrastructure, and solutions/workflows. This paper discusses those challenges and the ‘lessons’ learned during the implementation of this solution. Various value-added use cases are presented.

With the rise of machine learning and other artificial intelligence methods, the ability to digest data for potential application in the petroleum industry has increased tremendously. Data is seen as the key to reducing uncertainty in decisions for this capital‑intensive industry where the costs of bad or delayed decisions are huge. Thus, the right data delivered at the right time can revolutionize the industry and save precious time and money. Gaining insights from digital data in the highly technical exploration and production industry requires experience, knowledge, and awareness about the acquisition of the data. The technologies presented here aim to facilitate the decision‑making process while requiring less time and lower costs without sacrificing the accuracy of the data and still decreasing the probability of human errors. They show how data collected from different vantage points can be integrated with conventional data‑acquisition methods to help visualize and reduce the uncertainty of the subsurface. Developments in other industries in automation and electronics have enabled modernization and miniaturization of oilfield instruments. Our industry seeks ideas and methods that are reliable, convenient, and practical to inform and guide operations, filling the gaps where and when conventional data is not available. Recommended additional reading at OnePetro: www.onepetro.org. OTC 31327 Density Measurement of Three-Phase Flows Inside Vertical Piping Using Planar Laser-Induced Fluorescence by Amy Brooke McCleney, Southwest Research Institute, et al. OTC 31836 Subsea Multiphase Flowmeter Measurement Performance Assurance With an Applied Data Validation and Reconciliation Surveillance Methodology by Emmelyn Graham, BP, et al. SPE 208712 IADC Code Upgrade: Data Collection and Work Flow Required To Conduct Bit Forensics and Create Effective Changes in Practices or Design by William Watson, Shell, et al.