In Oil and gas industry, demand for DTS (distributed temperature sensing) completions installation is rapidly increasing in recent times. Currently multiple vendors are installing the DTS completions at field A for real-time water flood conformance monitoring and to meet target production rate. This paper discusses about the insights of data acquisition process and interpretation approaches to generate a standard operating procedure specific to Field A. DTS Completions are being installed in field A for few years, and they will be key in generating the optimum water injection profiles, proactive water flooding surveillance, injection/ production optimization and better reservoir management. However, the data acquisition, conditioning and interpretation is vendor specific. Moreover, the interpretation is influenced by the procedure followed for data collection, warm back period, the fluid at down hole, reservoir rock properties as well as the models being used for interpretation. In view of this multivendor installations available at field A, there is a need to standardize the data acquisition & interpretation process that will allow all reservoir and petroleum engineers in the field to analyze the DTS profiles regardless of the type of vendor DTS completion installations. Field A has 13 DTS Completions installed in multiple reservoirs from three different vendors, and the injection profiles for these wells are being generated. This paper summarizes the influence of key parameters and/or assumptions that play a major role on the interpretation results and their variability during data collection stage. Further, the comparison of the injection profile generated by DTS with mechanical PLT will be discussed, and the use of PLT data for fine-tuning the interpretation model. In addition, time-lapse injection profile generated at a specific well will be compared to understand the reasons for variation of it over a period. Additionally, the recommendations for improving/ optimizing the injection profile in few cases will be discussed including stimulations for better conformance and injection rate adjustments. In the conclusion, the proposed unified procedure covering data acquisition, format, storage, and standardized interpretation approach will be discussed. This is an attempt to standardize the DTS data collection and to unify the interpretation process among the multiple vendors in a specific Field A.
Developing mature reservoirs is associated with challenges and limitations both in surface and subsurface. In This paper we will tackle the area of developing reservoirs with Maximum Reservoir Contact (MRC) wells by introducing our experience in combining it with LEL completion and introduce initial results in unlocking the potential of our reservoir with reduced capital expenditure (CAPEX). Conventional wells with 3000 ft open hole length become more challenging as development of the field progresses and especially with drilling new wells. Congestion and anti-collision at surface and subsurface limitations arise more often let alone the cost, manpower and environmental issues. We are going to introduce Maximum reservoir contact MRC wells with extended open hole length of around 10,000 ft. which allow to maximize gain from this well while optimizing other parameters (well spacing, cost…etc.). Measurement of rate and pressure, pre and post acid stimulation controlled by LEL completion as well as comparison with conventional water injectors in the vicinity were performed before to evaluate the performance of the MRC well. It was confirmed that MRC well injectivity surpasses the conventional injectors with shorter open hole length in relatively tight areas of the reservoir which have low permeability especially if equipped with LEL completion. Initial results show Injectivity improvement compared with nearby water injectors plus additional gain in injectivity noticed after acid stimulation with the LEL completion. The MRC well performance after stimulation showed well head injection pressure decreased drastically after the acid stimulation with increased injected volumes of water. This shows that smart completion solutions well help to improve performance and optimize acid stimulation which highlights the importance of combining MRC with LEL to maximize gain and enhance performance to take the maximum benefit. Through our success with MRC well with LEL completion the community of petroleum engineering will be able to take decisions regarding implementing this method and technology to optimize their drilling which will have positive impact in cost, planning and environment.
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.
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