A Novel Holistic Workflow for Field Development Planning in Green Field Environment: A Case Study

Alkhatib, Mohamad; Ali, Abdulla Ali Al; Mukhtār, Muḥammad; Park, Sangseok; Ghorayeb, Kassem; Nasiri, Amir; Shah, Abdur Rahman; Ojha, Aditya

doi:10.2118/193140-ms

Cited by 6 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, portfolio considerations, such as evaluating the impact of the project on overall company The process (Figure 1) is a chronological series of events, with peer reviews for value assurance, denoted by project reviews (PRs) at the conclusion of each phase [42,45]. The conventional methodology involves sequentially calculating P10, P50, and P90 production forecasts [46], followed by determining the associated costs and revenues [47,48].…”

Section: Artificial Intelligence-centric Field Development Planningmentioning

confidence: 99%

Artificial Intelligence-Centric Low-Enthalpy Geothermal Field Development Planning

Clemens,

Chiotoroiu,

Corso

et al. 2024

Energies

View full text Add to dashboard Cite

Low-enthalpy geothermal energy can make a major contribution towards reducing CO2 emissions. However, the development of geothermal reservoirs is costly and time intensive. In particular, high capital expenditures, data acquisition costs, and long periods of time from identifying a geothermal resource to geothermal heat extraction make geothermal field developments challenging. Conventional geothermal field development planning follows a linear approach starting with numerical model calibrations of the existing subsurface data, simulations of forecasts for geothermal heat production, and cost estimations. Next, data acquisition actions are evaluated and performed, and then the models are changed by integrating the new data before being finally used for forecasting and economics. There are several challenges when using this approach and the duration of model rebuilding with the availability of new data is time consuming. Furthermore, the approach does not address sequential decision making under uncertainty as it focuses on individual data acquisition actions. An artificial intelligence (AI)-centric approach to field development planning substantially improves cycle times and the expected rewards from geothermal projects. The reason for this is that various methods such as machine learning in data conditioning and distance-based generalized sensitivity analysis assess the uncertainty and quantify its potential impact on the final value. The use of AI for sequential decision making under uncertainty results in an optimized data acquisition strategy, a recommendation of a specific development scenario, or advice against further investment. This approach is illustrated by applying AI-centric geothermal field development planning to an Austrian low-enthalpy geothermal case. The results show an increase in the expected value of over 27% and a reduction in data acquisition costs by more than 35% when compared with conventional field development planning strategies. Furthermore, the results are used in systematic trade-off assessments of various key performance indicators.

show abstract

Section: Artificial Intelligence-centric Field Development Planningmentioning

confidence: 99%

Artificial Intelligence-Centric Low-Enthalpy Geothermal Field Development Planning

Clemens,

Chiotoroiu,

Corso

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

Optimization of time, effort, and costs for well planning using standardization and automation of the well design process on a cloud-based solution

Srivastava,

Raizada

2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

The current well planning process for operators is capital-intensive that takes time and has a lot of discrete, disconnected steps. Well planners and engineers dedicate a significant amount of time and effort to analyze sub-surface and offset well data for trajectory planning, casing policy selection, casing design, torque & drag, hydraulics, time & cost analysis etc. For development wells and unconventional wells, it is a common scenario for drilling engineers to have a very clear understanding of how the well design shall look like as the oil & gas field is extremely well familiar. As an initial step towards standardization, a review and study of several well design processes were performed by interviewing several engineers, all around the globe. The study revealed that even though every engineer had their own way of working, there was an inherent workflow that could be standardized. This standardized workflow was then outlined with user experience development techniques to cater to the generic steps that well planners, engineers and managers had described. With implementation of this standardized workflow and UI/UX supported process, the next step was to build a cloud native solution which supports micro-services-based engineering calculations in the backend. This allowed to implement speed and scalability in the solution which can cater to various personas in a team. The next step was to automate the process for a development field. This was achieved by applying checkpoints in the workflow to identify an exploratory well vs. a development well, before calling a microservice. For a development field, the microservice architecture identifies the design aspects of an existing well and incorporates similar well trajectory turn points, casing policy, casing design, BHA, fluids, operational parameters etc. while honoring the surface hole location, targets, and datum reference of the new wells to be planned. This technique helped to not only automate the engineering calculations but also speed up the entire process of designing each well in under a minute. Apart from engineering calculations, a planning workflow is never complete without team governance, approvals from peers and supervisors, testing various scenarios and creating reports. As these tasks are an inherent part of the planning cycle, they were also incorporated in the workflow. The UX design process techniques ensured that these supplementary tasks were a part of the main workflow and did not interfere with the calculations. The solution has shown a tremendous increase in the work efficiency of user by reducing the planning efforts from days to minutes. The users can now focus on wells using management by exception as the entire design process can be automated. This also eliminates any unnecessary data entry and avoids errors.

show abstract

Robust EOR Screening Workflow for a Carbonate Green Field: A Case Study

Ali

Park

Mukhtār

et al. 2018

Day 2 Tue, November 13, 2018

View full text Add to dashboard Cite

Early assessment of enhanced oil recovery (EOR) potential in fields that are at early development stages is becoming more common in the oil industry, ensuring that investment decisions are consistent with the EOR deployment once the field reaches maturity. Well, facilities and monitoring design maybe influenced to accommodate the EOR implementation, thus reducing Capex and mitigating project exposure. Challenges arise, as expected, due to the limited information, particularly when the field has not yet been under production and dynamic information of connectivity, compartmentalization and reservoir extend is scarce. This paper describes the screening analysis performed on an onshore marginal green field in the UAE with four drilled wells and no production history with water injection considered on the approved development plan. The comprehensive screening workflow resulted on a narrow list of potential applicable EOR methods and their corresponding benefits allowing the operator to tailor development activities for early EOR de-risking and accelerated field deployment. A multi-dimensional approach was adopted using a combination of numerical, analytical methods and past EOR experience, to shortlist and rank the most attractive EOR development options, robustness of the selection (and ranking) was tested under the key reservoir uncertainties. WAG was identified as one of the better suited EOR processes (complemented the planned waterflood) along with miscible CO2 injection (with possible WAG applications).

show abstract

A Novel Holistic Workflow for Field Development Planning in Green Field Environment: A Case Study

Cited by 6 publications

References 12 publications

Artificial Intelligence-Centric Low-Enthalpy Geothermal Field Development Planning

Artificial Intelligence-Centric Low-Enthalpy Geothermal Field Development Planning

Optimization of time, effort, and costs for well planning using standardization and automation of the well design process on a cloud-based solution

Robust EOR Screening Workflow for a Carbonate Green Field: A Case Study

Contact Info

Product

Resources

About