Klie, Hector (DeepCast.ai) | Klie, Arturo (DeepCast.ai) | Rodriguez, Adolfo (OpenSim Technology) | Monteagudo, Jorge (OpenSim Technology) | Primera, Alejandro (Primera Resources) | Quesada, Maria (Primera Resources)
The Vaca Muerta formation in Argentina is emerging as one of the most promising resources of shale oil/gas plays in the world. At the current well drilling pace, challenges in streamlining data acquisition, production analysis and forecasting for executing timely and reliable reserves and resource estimations will be an overarching theme in the forthcoming years. In this work, we demonstrate that field operation decision cycles can be improved by establishing a workflow that automatically integrates the gathering of reservoir and production data with fast forecasting AI models.
We created a data platform that regularly extracts geological, drilling, completion and production data from multiple open data sources in Argentina. Data cleansing and consolidation are done via the integration of fast cross-platform database services and natural language processing algorithms. A set of AI algorithms adapted to best capture engineering judgment are employed for identifying multiple flow regimes and selecting the most suitable decline curve models to perform production forecasting and EUR estimation. Based on conceptual models generated from minimum available data, a coupled flow-geomechanics simulator is used to forecast production in other field areas where no well information is available. New data is assimilated as it becomes available improving the reliability of the fast forecasting algorithm.
In a matter of minutes, we are able to achieve high forecasting accuracy and reserves estimation in the Vaca Muerta formation for over eight hundred wells. This workflow can be executed on a regular basis or as soon as new data becomes available. A moderate number of high-fidelity simulations based on coupled flow and geomechanics allows for inferring production scenarios where there is an absence of data capturing space and time. With this approach, engineers and managers are able to quickly examine a feasible set of viable in-fill scenarios. The autonomous integration of data and proper combination of AI approaches with high-resolution physics-based models enable opportunities to reduce operational costs and improving production efficiencies.
The integration of physics-based simulations with AI as a cost/effective workflow on a business relevant shale formation such as Vaca Muerta seems to be lacking in current literature. With the proposed solution, engineers should be able to focus more on business strategy rather than on manually performing time-consuming data wrangling and modeling tasks.
For the vast majority of civilization, humans died in a world that looked very much like it did when they were born. But recently, the exponential growth of technology has fundamentally shifted the world's systems and the humans that occupy them. The oil and gas industry struggles with the question of how it will adopt and adapt in light of these technological advancements. A major driver in the current market uncertainty is choosing a technology that will provide optimal learning for the least amount of effort, money, and time.
In this paper, the benefit of using a technological advantage is explored from the point of view of generating type curves and forecasting well production. Traditional decline curve methods are founded on analytical expressions from the 1940s that are strictly based on empirical observations. Currently, engineers and analysts use a mix of these contemporaneous methods (and derivations thereof) and area expertise in their technical assessments to forecast well production. This type of analysis can introduce a level of bias which makes it very difficult, if not impossible, for two independently generated forecasts to be meaningfully reconciled against one another.
This two-part study explores a data-driven physio-statistical method for deriving production forecasts. The predictive analytical model underpinning this method has been trained on over 200,000 conventional and unconventional wells drilled in various plays with an extensive range of depositional environments, completion types, vintages, fluid properties, and operating conditions. Using solutions to differential equations to ensure that forecasts are generated honouring the fundamentals of fluid flow, provides accurate, unbiased, repeatable, and validated results.
This paper is based on work for a study area that encompasses 29 horizontal Montney gas wells in NE British Columbia. In part 1 of this study, production forecasts generated by the physio-statistical model (the Model) are compared to those generated by an experienced human reservoir engineer (HRE). The latter used a sophisticated commercially available decline curve analysis toolkit modified for unconventional reservoirs. Forecasted production volumes were compared against 12-months of actual production data and the suitability and limitations of the Model's forecasts are discussed.
As drilling automation has transformed from a blue sky vision to a reality, so has the industry's understanding of the depth and breadth of automation needs and opportunities. The focus is no longer solely on-bottom drilling performance but now considers the entire lifecycle of well construction. Sensors for automation Pragmatic implementations of digital twins Big data analytics that are proven Streaming analytics and edge solutions Systems approaches to automation including cyber security Discrete systems that deliver non-discrete benefits Human-centered design - industry 5.0 thinking (placing the human back in the loop) Oilfield logistics of the future Individual case studies within each session will be presented that provide a foundation for what is available today, and what will be available in the near term. This workshop will bring together operators, drilling data solution experts, as well as service company and drilling contractor personnel to identify and discuss challenges to accelerating adoption of these solutions in the industry. An SPE workshop is a multi-day event that fosters knowledge sharing in an intensive learning experience.
Data Analytics is progressively gaining traction as a viable resource to improve forecasts and reserve estimations in most prospective US shale plays. Part of those learnings has been tested for the reserves and resources estimation of the next worldwide top-class shale play, Vaca Muerta formation in Argentina. In this work, we rely on advanced artificial intelligence methods to automate workflows for production forecasting and reserve estimation in the Vaca Muerta formation. To achieve this goal, we develop a computational platform capable of integrating several sequential operations into a single automated workflow: (1) data gathering; (2) data preparation; (3) model fitting and forecasting and, (4) EUR estimation. As new data becomes available, each of these steps is performed automatically. The proposed platform also integrates with advanced business intelligence tools that aid at facilitating graphical interpretation and communication among specialists and decision makers. Hence, the suggested workflow can deliver production forecasts several magnitudes faster than traditional workflows while maintaining accurate and engineering sound results. Having fast and reliable forecast turnarounds allow for timely tracking key differences and commonalities among multiple shale plays to facilitate informed decision strategies in unconventional field evaluation and development.
As drilling automation has transformed from a blue sky vision to a reality, so has the industry's understanding of the depth and breadth of automation needs and opportunities. The focus is no longer solely on-bottom drilling performance but now considers the entire lifecycle of well construction. This workshop will examine the following key areas in terms of pragmatic solutions that are either available today or are coming in the near future. Individual case studies within each session will be presented that provide a foundation for what is available today, and what will be available in the near term. This workshop will bring together operators, drilling data solution experts, as well as service company and drilling contractor personnel to identify and discuss challenges to accelerating adoption of these solutions in the industry.
As a Flow Measurement Consultant at NEL, Craig’s responsibilities include working on a large variety of R&D, training and consultancy projects focused on single and multi-phase metering technology. He performs a variety of roles including project formulation, project management, technical lead, planning/delivering test work, data analysis and report writing. Craig has spent 10 years at NEL completing work in the technical areas of engineering design and review for custody transfer and fiscal metering measurement systems, measurement allocation philosophy documents, measurement system audits and financial exposure calculations. Currently, Craig is undertaking a doctorate degree at Coventry University in heavy oil and bitumen flow measurement and as part of the work has developed a Reynolds number correction method calculating flow and fluid physical properties in real-time. Risk management in thermal wellbore integrity can be promoted by the proper collection, processing and interpretation of data from various types of wellbore instrumentation.
Keeping Up With the Digital Age: What is Data Analytics all about? This interview explores the opportunities, challenges, and what young professionals need to know to have a rewarding career in drilling data analytics. Data story consumers are focused on summarized results and highlights instead of details of the analysis. It’s a data scientist’s responsibility to identify the significance of the data and to present it in a simple but scientific manner. Statoil's Acting head of Digital Centre of Excellence shares the company's digital road map.
Digitalization is going to impact every industry in the next 5–10 years. The oil and gas industry needs a lot more data scientists today than a year ago, so a person with the right qualifications and experience is the need of the industry today. Keeping Up With the Digital Age: What is Data Analytics all about? This interview explores the opportunities, challenges, and what young professionals need to know to have a rewarding career in drilling data analytics.
This article gives a succinct overview of artificial intelligence, its emerging opportunities, prospects, and challenges, and concludes with recommendations to accelerate the admission of AI into workflows. The term digital oil field has become a buzzword in the oil and gas industry these days, with the mention of it bringing up pictures of computers, flashy screens, and programming to mind. In reality, the concept goes beyond these. Digitalization is going to impact every industry in the next 5–10 years. The oil and gas industry needs a lot more data scientists today than a year ago, so a person with the right qualifications and experience is the need of the industry today.