Most of the crude oil is already recovered and discovering new oilfields tend to be challenging and difficult. Implementing an EOR method is essential to enhance the production life of mature oil fields and to make them economically more attractive. Especially, for heavy oil reservoirs chemical flooding is besides thermal methods promising. Only a limited number of alkali flood projects alone are reported worldwide. Phase screening represents the first step of experiments and gives information about the ability of various alkali solutions to generate in-situ surfactants at different concentration ranges. In this study, carbonate-based alkalis were screened on their effect on in-situ soap generation. Two oil reservoirs both located in the Matzen oil field (Austria) were observed, where an alkali flood project will be realized in the near future. In lab scale, were phase experiments with various concentrations of carbonate-based alkalis (sodium and potassium carbonate) screened at the water-oil-ratio 5:5. Formulations with synthetic and real softened brine were compared, using dead oil and viscosity-matched oil with cyclohexane. Samples were observed over time (100 days) to figure out their equilibrium at reservoir temperature. Afterwards large-scale samples were prepared and viscosity measurements performed. Potassium carbonate (K2CO3) is not well investigated in the literature as an alkali agent yet. It showed very promising results in all performed trials and generated remarkably more amounts of in-situ surfactants compared to Na2CO3, which is the most frequently used alkali performer. Additionally, in most concentrations the micro emulsion viscosities were lower. Thus, potassium carbonate might be an interesting candidate in future alkali applications.

As the field matures and overall field production decline, an accelerated and advanced selection of existing wells for workovers and sidetracks would be critical to meet with the increasing demand for production. Traditionally, an intensive effort is required to identify the right candidates and to ensure the technical and economic success of well interventions, infill-drilling locations and sidetrack locations. An advanced workflow is developed to automate the repetitive and less added value tasks such as data gathering and validation. The data set included historical production performance, static and dynamic reservoir and fluid properties, events and issues encountered within the evaluated wells and regions. The proposed solution allowed an integrated assessment of production enhancement opportunities through various consistent analytic computations, as well as machine learning techniques including Bayesian networks and time-series forecasting models. The automated process generates a comprehensive list of future well interventions including sidetrack candidates, infill-drilling locations and behind casing opportunities with an advanced scoring system and several technical (production performance and reserves) and financial KPIs (i.e. net present value and unit technical cost). Several dashboards built and adjusted with the involvement of various company departments. Lastly, highly ranked opportunities are incorporated into the business plan in accordance to field development targets as well as the availability technical resources (rigs, materials, well availability). The developed solution was tested and validated in a giant mature carbonate field with over 700 well strings in Offshore Abu Dhabi. The solution identified 20 times more feasible opportunities than the typical multidisciplinary team review in 75% less time duration. The automated workflows considered re-evaluating and selecting prime candidates with a reduced risk of failure, therefore improving the technical and economic value by 34%. The workflows are scheduled on daily basis giving a time-dependent assessment and expert monitoring system, which can also notify the operator when problems encountered. Instead of computationally heavy traditional numerical simulation models, the assessment of a large number of well count can be done within hours instead of months. The combination of physics and machine learning based models lead to the development of automated workflows to rank and determine the best candidates via a successful cost optimization and production enhancement.

Matured hydrocarbon fields are continuously deteriorating and selection of well interventions turn into critical task with an objective of achieving higher business value. Time consuming simulation models and classical decision-making approach making it difficult to rapidly identify the best underperforming, potential rig and rig-less candidates. Therefore, the objective of this paper is to demonstrate the automated solution with data driven machine learning (ML) & AI assisted workflows to prioritize the intervention opportunities that can deliver higher sustainable oil rate and profitability. The solution consists of establishing a customized database using inputs from various sources including production & completion data, flat files and simulation models. Automation of Data gathering along with technical and economical calculations were implemented to overcome the repetitive and less added value tasks. Second layer of solution includes configuration of tailor-made workflows to conduct the analysis of well performance, logs, output from simulation models (static reservoir model, well models) along with historical events. Further these workflows were combination of current best practices of an integrated assessment of subsurface opportunities through analytical computations along with machine learning driven techniques for ranking the well intervention opportunities with consideration of complexity in implementation. The automated process outcome is a comprehensive list of future well intervention candidates like well conversion to gas lift, water shutoff, stimulation and nitrogen kick-off opportunities. The opportunity ranking is completed with AI assisted supported scoring system that takes input from technical, financial and implementation risk scores. In addition, intuitive dashboards are built and tailored with the involvement of management and engineering departments to track the opportunity maturation process. The advisory system has been implemented and tested in a giant mature field with over 300 wells. The solution identified more techno-economical feasible opportunities within hours instead of weeks or months with reduced risk of failure resulting into an improved economic success rate. The first set of opportunities under implementation and expected a gain of 2.5MM$ with in first one year and expected to have reoccurring gains in subsequent years. The ranked opportunities are incorporated into the business plan, RMP plans and drilling & workover schedule in accordance to field development targets. This advisory system helps in maximizing the profitability and minimizing CAPEX and OPEX. This further maximizes utilization of production optimization models by 30%. Currently the system was implemented in one of ADNOC Onshore field and expected to be scaled to other fields based on consistent value creation. A hybrid approach of physics and machine learning based solution led to the development of automated workflows to identify and rank the inactive strings, well conversion to gas lift candidates & underperforming candidates resulting into successful cost optimization and production gain.