Flourishing Production Optimization thru the Development of an Enhanced Testing Validity Methodology

2021 ◽  
Author(s):  
Rakan Al Yateem ◽  
Mohammad Al-Kadem ◽  
Suliman Alodhiani ◽  
Majed Kishi
Keyword(s):  
Decision Making ◽  
Test Data ◽  
Test Validity ◽  
Testing Procedure ◽  
Production Optimization ◽  
Testing Equipment ◽  
Well Performance ◽  
Improve Measurement ◽  
Rate Testing ◽  
Rate Test

Abstract Rate testing has evolved over the years. From a simple composite separator system, the scope of rate testing has morphed into a broad spectrum of sophisticated downhole and surface technologies. Knowing well behavior, performance, and associated rate are the key factors of operating an entire field with the most reliable operating strategy, assuring maximum well-life time. In regard to well modeling and optimization, valid rate test data are crucial to predict well performance efficiently. An in-house rate testing mechanism was developed to ensure proper delivery, accuracy, and validity of rate tests. The mechanism comprises a rate testing procedure and decision-making tree. The rate testing procedure includes regular checks of rate testing data reports. Also, the immediate resolution of rate testing equipment or communication issues is implemented through the utilization of an MPFM Advanced Monitoring System with automated logics. A decision-making tree constitutes pre- and post-testing process phases. The pre-testing process phase involves an assessment for rate testing readiness in terms of testing equipment and communication. The post-testing process phase includes an assessment for testing operation and rate test validity where rate test data are checked and validated based on production operational status. The enhanced testing mechanism is a user-friendly guideline for testing requirements to ensure the completion of tests captured from testing equipment. The proper implementation of this rate testing mechanism enabled a high quality and accuracy of rate test data, resulting in an increase in rate testing validity by 30%. Also, the rate testing mechanism inspired a culture of continuous effective communication for all involved parties during the testing operation. The decision-making tree transforms the validation process from subjective thinking to a systematic workflow while integrating data from nearby wells with similar behavior. A high ownership level is exhibited by taking the immediate resolution of issues results in achieving high rate testing validity percentage. Running the process through standardized operating procedures is critical in generating consistent and predictable results of well performance. Additionally, accurate optimization and prediction of well performance have been realized by feeding the well model's data before and after attaining valid rate test data, which attests to the quality of the proposed rate testing mechanism. Considering the importance of having a strategic rate testing mechanism, it is highly advised to have more frequent measurements to raise the accuracy of the measurements presented. An ideal strategic rate testing mechanism has to be economical enough to be placed in many production wells, allow the tests to be performed in an organized manner, improve measurement accuracy, and, more importantly, achieve automated and supervised well tests processes.

Artificial Intelligence Assisted Well Portfolio Optimization - An Automated Reservoir Management Advisory System to Maximize the Asset Value - Case Study from ADNOC Onshore

2021 ◽  
Author(s):  
Subba Ramarao Rachapudi Venkata ◽  
Nagaraju Reddicharla ◽  
Shamma Saeed Alshehhi ◽  
Indra Utama ◽  
Saber Mubarak Al Nuimi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hybrid Approach ◽  
Simulation Models ◽  
Production Optimization ◽  
Added Value ◽  
Risk Scores ◽  
Well Performance ◽  
Field Development ◽  
Advisory System ◽  
Gas Lift

Abstract 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.

The Limitation of Reservoir Saturation Logging Tool in a Case of a Deeper Reservoir Flow into a Shallower Reservoir Within the Same Wellbore

2021 ◽  
Author(s):  
P. Merit Ekeregbe
Keyword(s):  
Decision Making ◽  
Flow Condition ◽  
Oil And Gas ◽  
Static Condition ◽  
Oil And Gas Industry ◽  
Well Performance ◽  
Liquid Loading ◽  
Critical Rate ◽  
Logging Tool ◽  
Historical Production

Abstract Saturation logging tool is one key tool that has been successfully used in the Oil and Gas Industry. As important as the tool is, it should not be mistaken for a decision tool, rather it is a tool that aids decision making. Because the tool aids decision making, the decision process must be undertaken by interdisciplinary team of Engineers with historical knowledge of the tool and the performance trend of the candidate well and reservoir. No expertise is superior to historical data of well and reservoir performance because the duo follows physics and any deviation from it is attributable to a misnomer. The decision to re-enter a well for re-perforation or workover must be supported by historical production and reasonable science which here means that trends are sustained on continuous physics and not abrupt pulses. Any interpretation arising from saturation logging tools without subjecting same to reasonable science could result in wrong action. This paper is providing a methodology to enhance thorough screening of candidates for saturation logging operations. First is to determine if the candidate well is multilevel and historical production above critical gas rate before shut-in to screen-out liquid loading consideration. If any level is plugged below any producing level, investigate for micro-annuli leakage. All historical liquid loading wells should be flowed at rate above critical rate and logged at flow condition. Static condition logging is only good for non-liquid loading wells. The use of any tool and its interpretation must be subjective and there comes the clash between the experienced Sales Engineer and the Production/Reservoir Engineer with the historical evidence. A simple historical trending and analysis results of API gravity and BS&W were used in the failed plug case-study. Further successful investigation was done and the results of the well performance afterwards negated the interpretation arising from the saturation tool which saw the reservoir sand flushed. The lesson learnt from the well logging and interpretation shows that when a well is under any form of liquid loading, interpretation must be subjective with reasonable science and historical production trend is critical. It is recommended that when a well is under historical liquid loading rate, until the rate above the critical rate is determined, no logging should be done and when done, logging should be at flow condition and the interpretation subject to reasonable system physics.

Production Optimization in Mature Field Through Scenario Prediction Using a Representative Network Model: A Rapid Solution Without Well Intervention

2021 ◽  
Author(s):  
Edwin Lawrence ◽  
Marie Bjoerdal Loevereide ◽  
Sanggeetha Kalidas ◽  
Ngoc Le Le ◽  
Sarjono Tasi Antoneus ◽  
...  
Keyword(s):  
Network Model ◽  
Test Data ◽  
Production Optimization ◽  
Operating Conditions ◽  
Fine Tuning ◽  
Well Test ◽  
Production Rates ◽  
Mature Field ◽  
Gas Lift ◽  
Field Production

Abstract As part of the production optimization exercise in J field, an initiative has been taken to enhance the field production target without well intervention. J field is a mature field; the wells are mostly gas lifted, and currently it is in production decline mode. As part of this optimization exercise, a network model with multiple platforms was updated with the surface systems (separator, compressors, pumps, FPSO) and pipelines in place to understand the actual pressure drop across the system. Modelling and calibration of the well and network model was done for the entire field, and the calibrated model was used for the production optimization exercise. A representative model updated with the current operating conditions is the key for the field production and asset management. In this exercise, a multiphase flow simulator for wells and pipelines has been utilized. A total of ∼50 wells (inclusive of idle wells) has been included in the network model. Basically, the exercise started by updating the single-well model using latest well test data. During the calibration at well level, several steps were taken, such as evaluation of historical production, reservoir pressure, and well intervention. This will provide a better idea on the fine-tuning parameters. Upon completion of calibrating well models, the next level was calibration of network model at the platform level by matching against the platform operating conditions (platform production rates, separator/pipeline pressure). The last stage was performing field network model calibration to match the overall field performance. During the platform stage calibration, some parameters such as pipeline ID, horizontal flow correlation, friction factor, and holdup factor were fine-tuned to match the platform level operating conditions. Most of the wells in J field have been calibrated by meeting the success criterion, which is within +/-5% for the production rates. However, there were some challenges in matching several wells due to well test data validity especially wells located on remote platform where there is no dedicated test separator as well as the impact of gas breakthrough, which may interfere to performance of wells. These wells were decided to be retested in the following month. As for the platform level matching, five platforms were matched within +/-10% against the reported production rates. During the evaluation, it was observed there were some uncertainties in the reported water and gas rates (platform level vs. well test data). This is something that can be looked into for a better measurement in the future. By this observation, it was suggested to select Platform 1 with the most reliable test data as well as the platform rate for the optimization process and qualifying for the field trial. Nevertheless, with the representative network model, two scenarios, reducing separator pressure at platform level and gas lift optimization by an optimal gas lift rate allocation, were performed. The model predicts that a separator pressure reduction of 30 psi in Platform 1 has a potential gain of ∼300 BOPD, which is aligned with the field results. Apart from that, there was also a potential savings in gas by utilizing the predicted allocated gas lift injection rate.

scholarly journals Neural network activation similarity: a new measure to assist decision making in chemical toxicology

2020 ◽  
Vol 11 (28) ◽  
pp. 7335-7348 ◽  
Author(s):  
Timothy E. H. Allen ◽  
Andrew J. Wedlake ◽  
Elena Gelžinytė ◽  
Charles Gong ◽  
Jonathan M. Goodman ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Deep Learning ◽  
Test Data ◽  
High Performance ◽  
Chemical Binding ◽  
Biological Targets ◽  
Network Activation ◽  
Chemical Toxicology

Deep learning neural networks, constructed for the prediction of chemical binding at 79 pharmacologically important human biological targets, show extremely high performance on test data (accuracy 92.2 ± 4.2%, MCC 0.814 ± 0.093, ROC-AUC 0.96 ± 0.04).

scholarly journals Sistem Pakar dalam Mengidentifikasi Minat Vokasi Menggunakan Metode Certainty Factor dan Forward Chaining

2020 ◽  
Author(s):  
Kurniawan Jefdy ◽  
Sarjon Defit ◽  
Yuhandri Yunus
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Expert System ◽  
Test Data ◽  
System Application ◽  
Vocational Interests ◽  
Personality Types ◽  
Certainty Factor ◽  
Continuing Higher Education ◽  
Vocational Interest

Developing an expert system application in providing an overview of the interests of students to help decision making interests in the vocational field so that they are right on target in choosing a major. In this study, using the Certainty Factor method and the Fordward Chaining method where this expert system can help experts identify vocational interests based on the characteristics of vocational interest in students. The personality types used to determine the type of vocational interest are Tangible, Thinking, Flexible, and Entrepreneur. The results of system calculations with expert decisions are worth 80% of the 4 test data, so a good level of accuracy is obtained. The resulting expert system can help students quickly provide an overview of vocational interest in making department decisions in continuing higher education, can carry out online consultations, document files, and can be used as a consultation portal for students.

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