Ahmed, Syed (Saudi Arabian Oil Company, Saudi Aramco) | Al-Zubail, Ahmad (Saudi Arabian Oil Company, Saudi Aramco) | Al-Jeshi, Majed (Saudi Arabian Oil Company, Saudi Aramco) | Yousef, Khaled (Saudi Arabian Oil Company, Saudi Aramco) | Musabbeh, Alya (Saudi Arabian Oil Company, Saudi Aramco) | Mousa, Saad (Saudi Arabian Oil Company, Saudi Aramco) | Bukhari, Adeeb (Saudi Arabian Oil Company, Saudi Aramco) | Seraihi, Emad (Saudi Arabian Oil Company, Saudi Aramco) | Alamri, Sultan H. (Saudi Arabian Oil Company, Saudi Aramco)
This paper describes integrated solution that leverages Industrial Revolution 4.0 to sustain crude quality specifications for Saudi Aramco supply chain covering more than 50 GOSPs (Gas Oil Separation Plants), Pipelines, and Terminals. Sustaining crude quality specifications such water content (BS&W), salt content, etc. for the Arabian Crudes (Arab light, Arab Extra Light etc.) requires big data analysis across the supply chain. To address this challenge, Saudi Aramco developed a customized solution called Crude Quality Monitoring Solution (CQMS) which leverages 800 critical data streams every minute (PI values), classifies the data according to the risk level impacting crude quality specifications. Three developed risk levels are leading proactive, lagging proactive, and lagging reactive, each of which has a defined acceptable risk matrix. Each risk matrix initiates automated notifications for corrective actions proactively. Moreover, patterns and operational events can be easily recognized in the solution visually. The paper also describes several examples where the solution notifications have proactively remediated process disturbances by up to 20% at upstream and downstream facilities while ensuring asset integrity. The solution deployment has also substantially improved the operational efficiency across the network by benchmarking critical data streams. Saudi Aramco is continuing to enhance the solution capabilities to ensure maximization of the crude network.
Rubio, Erismar (ADNOC Onshore) | Reddicharla, Nagaraju (ADNOC Onshore) | Dilsiz, Melike (ADNOC Onshore) | Al-Attar, Mohamed Ali (ADNOC Onshore) | Raj, Apurv (Weatherford) | Soni, Sandeep (Weatherford) | Sabat, Siddharth (Weatherford) | Isambertt, Jose (Weatherford)
This paper describes an efficient, accurate, and timesaving approach for setting well allowable using advanced and automated workflows in a digital oil field with more than 300 producing and injecting strings from multi-layered reservoirs having varied reservoir characteristics. This paper provides an insight on the usage of ADNOC shareholders guidelines, well characteristics, surface facility constraints, and integrated asset models to compute the well allowable rate.
An integrated asset operations model (IAOM) within a digital framework provides an automation of engineering approach where shareholder/reservoir management guidelines, in conjunction with a calibrated well and network models, are used to improve efficiency and accuracy of setting wells allowable. This process incorporates the interaction among various components, including wellbore dynamics (Inflow and outflow performance), surface network backpressure effect, and complex system constraints. "System Efficiency and Well Availability" factors as well as predicted well parameters such as GOR and watercut. This advance workflow computes the rate that can be delivered from each well corresponding to each guideline and constraint, thereby providing key inputs to various business objective scenarios for production efficiency improvement.
This automated "Setting Well Allowable" workflow, using an IAOM solution in a digital framework, has enabled the asset to identify true potential of wells and overcoming potential challenges of computational time saving while identifying opportunities. This automated validation workflows ensured usage of updated and validated well models, allowing effective use of the well test information and real time data for further analysis and sensitivities.
The use of the automated workflow has reduced the time to compute the well allowable rates and well technical rates by more than 50%. This workflow prevented engineers from performing tedious manual calculations on a well-by-well basis, therefore engineers focus on engineering and analytical problems rather than collecting data. Additionally, this robust engineering approach provides users with key information associated with a well's performance under various guideline index such as potential rates, well technical rate, minimum backpressure rate, rate to maintain drawdown/ minimum bottom hole pressure limit to ensure a homogenous reservoir withdraw to avoid pressure sink areas. This work process also highlights the wells with increased watercut (WC) and gas oil ratio (GOR), thus providing crucial information for deteriorating well performance. A short-term forecasting with diagnostic curve fitting and trend analysis enabled users to validate deliverability of allowable rates in a calibrated network model scenario, thereby incorporating potential surface constraints and facility bottlenecks.
The robustness of advanced and automated setting of well allowable workflow enables the operator to establish well performance with a solid engineering analysis base, and thereby unlocks key opportunities for saving cost, computational time and assuring short-term production mandate deliverables. This approach supports standardization of the work process across the whole organization.
Javid, Khalid (ADNOC Offshore) | Ettireddi, Srinivas (ADNOC Offshore) | Mokhtar Hafez, Yahia (ADNOC Offshore) | Hossni Ali, Mohamed (ADNOC Offshore) | Marin Centurion, Pedro Ronaldo (ADNOC Offshore) | Gonzalez Cerrada, Luis Oscar (ADNOC Offshore) | Mohamed, Mohamed Sayed (ADNOC Offshore) | Zahaf, Kamel (ADNOC Offshore) | Ibrahim Alhosani, Khalil (ADNOC Offshore) | Al Qamzi, Abdulla Gharib (ADNOC Offshore)
This Green Field was recently commissioned and it was put on production last year. It is a model Digital Oil Field having two artificial islands built to drill all its wells with smart completions like ICVs, ICDs and Permanent Downhole Gauges in all Oil & Gas Producer Wells. This paper will describe the benefits of real-time data utilization for better and most efficient Field/Wells Monitoring along with Better and quick decision-making. Flow tests are performed 2-3 times a week using Multi phase flow meter (MPFM) for each of its wells.
Smart and Innovative Dashboard have been created for best screening and grouping of wells as per predefined business rules and alerting Asset Engineers for any wells, which are close to violating any of the Reservoir Management Guidelines, and therefore timely decisions, are made to avoid those violations. Miscible Gas Injection was started from the early days of Field life. Gas Tracers are planned to be injected by the end of this year to achieve the successful and improved surveillance and understandingly Reservoirs and better plan the future wells locations and completion strategy.
All Field/well Shut Down duration opportunities are utilized for Pressure Build-up analysis for Oil Producer wells and therefore considerable cost of running Memory gauges and intervention operations is also saved.
Successful Digital Oil Field is a result of collaborative and multiple discipline Team Work. Lessons learned and recommendation for any new Digital Oil Field are also presented in the paper.
Innovative Dashboards have been created to for best screening of wells and alerting for any wells, which are close to violating any of the Reservoir Management Guidelines, and therefore timely decisions, are made to avoid those violations.
The standardized data repository is developed as data integration layer using OSI PI platform products to seamlessly merge real time data with manual data to create a single data reference architecture. This has been a powerful enhancement to ensure a single version of the truth.
Contreras Perez, David Rafael (OMV E&P GmbH - Abu Dhabi) | Al Zaabi, Ruqaya Abdulla (ADNOC Offshore - GUL) | Viratno, Bernato (OMV E&P GmbH - Abu Dhabi) | Sellar, Christopher (OMV E&P GmbH - Abu Dhabi) | Susanto, Maria Indriaty (OMV E&P GmbH - Abu Dhabi)
The rationale of structural uncertainty analysis in reservoir modeling is to quantify the range of probable Gross Rock Volume (GRV) s and searchfor the means to reduce this range as much as possible. This task considers running different scenarios and/or structural configurations based on the observed mismatch between structural depth estimation from seismic mapping and stratigraphic tops derived from well data. Integrated multi-disciplinary teams can collaboratively eliminate reservoir uncertainties at the well location, however uncertainty remains in the interwell area. The challenge for any reservoir characterization team is to share expertise across disciplines in order to mitigate the lack of information with scientific reasoning. In this way the range of uncertainties impacting business decisions, development scenarios or data acquisition plans are minimised. The workflow summarized here is an example of how to utilize structural elements from existing wells to quantify intrinsic GRV uncertainty while building static models. Offshore Field developments usually have a bigger horizontal well count than the ideal vertical penetrations and this case study is no exception in this case study. The ultimate goal of this publication is to generate the inputs required for a more realistic set of structural realizations that fulfil all of the current understanding from horizontal well placement and their intrinsic structural uncertainty.
Pathak, Shashank (Cairn Oil & Gas, Vedanta Ltd.) | Ranjan, Ashish (Cairn Oil & Gas, Vedanta Ltd.) | Bohra, Avinash (Cairn Oil & Gas, Vedanta Ltd.) | Vermani, Sanjeev (Cairn Oil & Gas, Vedanta Ltd.) | Tiwari, Shobhit (Cairn Oil & Gas, Vedanta Ltd.) | Shrivastava, Pranay (Cairn Oil & Gas, Vedanta Ltd.) | Nagar, Ankesh (Cairn Oil & Gas, Vedanta Ltd.) | Ahsan, Mohammad Ayaz (Cairn Oil & Gas, Vedanta Ltd.) | Modi, Jaya Kumari (Cairn Oil & Gas, Vedanta Ltd.) | Upadhyay, Akhilesh (Cairn Oil & Gas, Vedanta Ltd.)
Mangala, Bhagyam & Aishwaraya Oil fields with ~669 wells produce about 20% of India's domestic crude production. As a part of production enhancement & sustenance activity various stimulation treatments were implemented from the initial development phase of these fields. Over time as these fields went from water flood to polymer flood, several modifications were made in stimulation treatment design to maintain the effectiveness of the stimulation treatments. Over last 8 years over 1100 stimulation treatment were executed in these field with most of the information kept within the treatment specific reports. To tap the value from this huge volume of information, a data structure was prepared to extract important learnings from these treatments. This paper details the workflow which was adopted to compile the historical unstructured data in a structure and details the crucial findings & learnings from the advanced data analytics applied on this data.
The primary objective of this work was to put unstructured data from 1100 stimulation treatment into a structured format. Information specific to the treatment design such as treatment fluid, volumes, concentrations, additives, pumping technique, soaking time etc. were compiled. This was also followed with wells specific information such as completion details, formation type, pre and post stimulation production/injection rates etc. Since the information volume was large and the data was scattered, a stimulation job code was defined which carried all the relevant information about any stimulation treatment in a simpler, scalable and structured format. The work was followed with advanced data analytics to extract value from this historical data spread over last 8 years. Stimulation performance indexes were defined to evaluate effectiveness of all these treatments which helped to identify root causes which led to some of the most successful stimulation treatments and helped to delineate learnings from unfavorable results.
The work identified the primary factors impacting the performance of stimulation treatments from broad field level to well & formation specific learnings. The overall findings included job specific learnings, findings specific to treatments fluid such as composition of chelating agent and its impact, concentration of HCl on injection improvement etc. as well as operational aspects while executing these jobs.
There are numerous technical papers on effectiveness on stimulation treatments and their design, this paper compiles the learnings from over 1100 stimulation treatments which provides a bridge between the theory and the practice while it also provides crucial insights on the operational aspects of these treatment as well which can impact the performance of these treatments. The paper also details the novel workflow adopted to structure the unstructured historical data to create substantial value.
Mawlad, Arwa Ahmed (ADNOC Onshore) | Mohand, Richard (ADNOC) | Agnihotri, Praveen (ADNOC Onshore) | Pamungkas, Setiyo (ADNOC) | Omobude, Osemoahu (ADNOC) | Mustapha, Hussein (Schlumberger) | Freeman, Steve (Schlumberger) | Ghorayeb, Kassem (American University of Beirut) | Razouki, Ali (Schlumberger)
Challenges associated with volatile oil and gas prices and an enhanced emphasis on a cleaner energy world are pushing the oil and gas industry to re-consider its fundamental existing business-models and establish a long-term, more sustainable vision for the future. That vision needs to be more competitive, innovative, sustainable and profitable. To move along that path the oil and gas industry must proactively embrace the 4th Industrial Revolution (oil and gas 4.0) across every part of its business. This will help to overcome time constraints in the understanding and utilization of the terabytes of data that have been and are continuously being produced. There is a clear need to streamline and enhance the critical decision-making processes to deliver on key value drivers, reducing the cost per barrel, enabling greater efficiencies, enhanced sustainability and more predictable production.
Latest advances in software and hardware technologies enabled by virtually unlimited cloud compute and artificial intelligence (AI) capabilities are used to integrate the different petro-technical disciplines that feed into massive reservoir management programs. The presented work in this paper is the foundation of a future ADNOC digital reservoir management system that can power the business for the next several decades. In order to achieve that goal, we are integrating next generation data management systems, reservoir modeling workflows and AI assisted interpretation systems across all domains through the Intelligent Integrated Subsurface Modelling (IISM) program. The IISM is a multi-stage program, aimed at establishing a synergy between all domains including drilling, petrophysics, geology, geophysics, fluid modeling and reservoir engineering. A continuous feedback loop helps identify and deliver optimum solutions across the entire reservoir characterization and management workflow. The intent is to dramatically reduce the turnaround time, improve accuracy and understanding of the reservoir for better and more timely reservoir management decisions. This would ultimately make the management of the resources more efficient, agile and sustainable.
Data-driven machine learning (ML) workflows are currently being built across numerous petro-technical domains to enable quicker data processing, interpretation and insights from both structured and unstructured data. Automated quality controls and cross domain integration are integral to the system. This would ensure a better performance and deliver improvements in safety, efficiency and economics. This paper highlights how applying artificial intelligence, automation and cloud computing to complex reservoir management processes can transform a traditionally slow and disconnected set of processes into a near real time, fully integrated, workflow that can optimize efficiency, safety, performance and drive long term sustainability of the resource.
Estimating the lateral heterogeneity of geochemical properties of organic rich mudrocks is important for unconventional resource plays. Mature regions can rely on abundant well data to build empirical relationships and on traditional geostatistical methods to estimate properties between wells. However, well penetration in emerging plays are sparse and so these methods will not yield good results. In this case, quantitative seismic interpretation (QSI) might be helpful in estimating the desired properties. In this study, we use QSI based on a rock physics template in estimating the uncertainty of the geochemical properties of organic mudrocks of the Shublik Formation, North Slope, Alaska. A rock physics template incorporating lithology, pore fraction, kerogen fraction, and thermal maturity is constructed and validated using well data. The template clearly shows that the inversion problem is non-unique. Inverted impedances cubes are estimated from three seismic angle gathers (near with angles between 0° and 15°, mid with angle gathers between 15° and 30°, and far with angle gathers between 30° and 45°). The inversion is done using a model-based implementation with an initial earth model derived from the seismic velocity model used in the processing phase. By combining the rock physics template and the results of seismic inversion, multiple realizations of total organic content (TOC), matrix porosity, and brittleness index are generated. These parameters can be used for sweet spot detection. Lithological results can also be used as an input for basin and petroleum system modeling.
Consider the sheer diversity and magnitude of data related to approximately 1000 reservoirs mapped to 100+ oil and gas assets across three land masses. Now spread that across more than 10 operators and 3000 wells. This is the challenge underlying the hydrocarbon scene of Malaysia. Technical and operational problems associated with such a scenario include mature reservoirs supporting wells that are more than 20+ years old, well integrity issues, late life declines, increasing frequency of unplanned deferments, sand production, scaling and souring, to name a few. The high cost factors, due to the off-shore environment, combined with the uphill task to sustain production, adds to the challenge of Managing Malaysia's assets.
As regulator and host authority for all hydrocarbon resources in Malaysia, Malaysian Petroleum Management (MPM) is using digital technology to design and develop production surveillance strategies to tackle the situation. PACE (PETRONAS Asset Performance Centre) has been developed as an integrated platform to deliver solutions that encompass data consolidation to visualization to KPI management, monitoring and tracking. Designed essentially to improve, enhance & speed up decision and aid decision making process, PACE is central to developi ng a holistic point of view (HPOV).
The key methodology used to analyze local/ national trends and leveraging them to drive performance lies in extensive development of benchmarks & setting up of automated tracking mechanisms that will trigger alert response system in case of deviation. The multi operator production environment, a full spectrum of sub-surface complexity, varied development strategies depending upon the time and operating company presents itself as a perfect set up for implementing the technology.
A simple example to illustrate the point can be seen in production enhancement and idle well restoration activities. There are literally hundreds of jobs happening on yearly basis which engage a number of vendors, technologies, equipment and can be categorized in various of job types.
Benchmarking these activities even on a national level helps immensely to identify clearly what is working and what is not when it comes to job efficiency, operator delivery and return against investment. The results can than drive strategy for replication and improving in the following years. The next level would be introduction of Regional Benchmarks initiating collaboration across borders.
The architecture of the system has been designed to act as integrator that can accept, assimilate and integrate. As time moves we plan to augment the information related to costs, facilities performance, maintenance commitments etc.. The surveillance based strategy setting will be a natural outcome. Authors propose that the designed system can be applied to NOCs or major operators owning and operating multiple assets to drive better efficiency and higher profits
Dell'Aversana, Paolo (Eni SpA) | Servodio, Raffaele (Eni SpA) | Bottazzi, Franco (Eni SpA) | Carniani, Carlo (Eni SpA) | Gallino, Germana (Eni SpA) | Molaschi, Claudio (Eni SpA) | Sanasi, Carla (Eni SpA)
In this paper, we introduce a new technology permanently installed on the well completion and addressed to a real time reservoir fluid mapping through time-lapse electric/electromagnetic tomography while producing and/or injecting. Our technology consists of electrodes and coils installed on the casing/liner in the borehole/reservoir section of the well. We measure the variations of the electromagnetic fields caused by changes of the fluid distribution in a wide range of distances from the well, from few meters up to hundreds meters. The data acquired by our technology are processed and interpreted through an integrated software platform that combines 3D and 4D geophysical data inversion with a Machine Learning platform equipped with a complete suite of classification/prediction algorithms. Every time new data are acquired, they are fully integrated with the previous database, and used for decreasing the level of uncertainty about the dynamic model of the reservoir. In order to clarify the potential impact of such system on reservoir management, we apply this methodology to a synthetic data set. We discuss a simulation of a scenario where the waterfront approaches the wells during oil production. The goal of our test is to show how to combine our technology with Machine Learning to make robust predictions about the water table variations around the production wells.
Roig, Carlos (Schlumberger) | Elmansoury, Alaa (Schlumberger) | Glushchenko, Anna (Schlumberger) | Vargas, Jose Edgardo (ADNOC) | Utami, Yeni Prasetyo (ADNOC) | Seo, Youngtak (ADNOC) | Al Ghafri, Thuraya Mohammed (ADNOC) | Liu, James (ADNOC) | Kim, Dongoh (ADNOC)
Good quality seismic data are essential for seismic reseirvoir characterization, from seismic applications (wavelet estimation, low frequency model building, seismic velocity calibration and time to depth conversion) to petro-physical interpretation, rock physics modelling and hard data in deostatistical modeling. Surface seismic data need to possess the necessary level of quality to make the advanced inversion techniques successful in providing the reservoir characterization results to be input into the static models. Interbed multiples occur in all types of seismic datasets and are challenging to address, directly and negatively affecting structural and stratigraphic mapping, as well as extracting concise petrophysical attributes from the seismic data. This is particularly a problem for deep and fractured reservoirs, where strong internal multiple contamination increases the ambiguity of the interpretation and inversion of the seismic data, leading to misleading reservoir characterization, ultimately affecting well construction and placement. These interbed multiples can appear at a very similar timing and amplitudes to primaries due to comparable velocities which makes it difficult to differentiate from primary energy in areas with relatively flat geology. In current paper we will demonstrate the demultiple workflow for low relief structures in Abu Dhabi. To address the specific challenges, Extended Interbed Multiple Prediction or a combination of methods presented. The multiple attenuation workflow described in this work follows forward modelling and adaptive subtraction with primary protection approach, improves the preconditioning of seismic for pre-stack inversion over the target oilfield. "The output seismic data after the optimal multiple attenuation workflow provide a superior seismic inversion and a suitable input for reservoir assessment and delineation of the thin carbonates reservoirs present in UAE" (Melo, Glushchenko et al. 2017).