Model Prediction of Critical Gas Rate With Water-Cut Reversal Effect

2021 ◽  
Author(s):  
Merit P. Ekeregbe
Keyword(s):  
Model Prediction ◽  
Field Data ◽  
Dynamic Test ◽  
Late Life ◽  
Well Performance ◽  
Critical Rate ◽  
Reservoir Performance ◽  
Rate Prediction ◽  
Liquid Load ◽  
Water Cut

Abstract Accurate prediction of gas critical rate is critical to the successful management of gas wells. This paper covers the prediction of gas critical rate and presents limitations of old models with gas condensate wells with water-cut reversal. Comparison of prediction methods or models with this new method will be explained using field data of condensate wells. The effect and relation of water-cut with critical gas rate determination will be presented and the best method that universally meets changing conditions of the well will be tested with field data. Any method that must be acceptable must meet the dynamics of the well. No static model can predict accurately a dynamic well and reservoir performance. The old models of critical gas rate prediction show a static outlook, probably see the beginning of the well-life and cannot predict correctly when the fluid phases change in gravity. The late life prediction of the well performance is much more critical than the early life when the well has sufficient energy. The production envelope is more critical at depletion than at when the reservoir energy just kick. Therefore, any model prediction must be dynamic. The results from the old models show that they fail the dynamic test of the well performance. This limitation makes those model unusable in a late life of the well when water cut increases. This study has revealed a method or a model for critical rate prediction that is accurate throughout the life of the well. The effect of water cut reversal is well tracked with this new model whereas the static nature of other models predicts a wrong minimum rate at a liquid load up rate. The field data reveals that the dynamic nature of well and reservoir performance can only be understood dynamically.

Model Prediction of Critical Gas Rate with Water-Cut Reversal Effect

2021 ◽  
Author(s):  
Merit P. Ekeregbe
Keyword(s):  
Model Prediction ◽  
Field Data ◽  
Dynamic Test ◽  
Late Life ◽  
Well Performance ◽  
Critical Rate ◽  
Reservoir Performance ◽  
Rate Prediction ◽  
Liquid Load ◽  
Water Cut

Abstract Accurate prediction of gas critical rate is critical to the successful management of gas wells. This paper covers the prediction of gas critical rate and presents limitations of old models with gas condensate wells with water-cut reversal. Comparison of prediction methods or models with this new method will be explained using field data of condensate wells. The effect and relation of water-cut with critical gas rate determination will be presented and the best method that universally meets changing conditions of the well will be tested with field data. Any method that must be acceptable must meet the dynamics of the well. No static model can predict accurately a dynamic well and reservoir performance. The old models of critical gas rate prediction show a static outlook, probably see the beginning of the well-life and cannot predict correctly when the fluid phases change in gravity. The late life prediction of the well performance is much more critical than the early life when the well has sufficient energy. The production envelope is more critical at depletion than at when the reservoir energy just kick. Therefore, any model prediction must be dynamic. The results from the old models show that they fail the dynamic test of the well performance. This limitation makes those model unusable in a late life of the well when water cut increases. This study has revealed a method or a model for critical rate prediction that is accurate throughout the life of the well. The effect of water cut reversal is well tracked with this new model whereas the static nature of other models predicts a wrong minimum rate at a liquid load up rate. The field data reveals that the dynamic nature of well and reservoir performance can only be understood dynamically.

Late Life Field Management with Instrumentation Failure. Modelling Predictions and Successful Validations of Late Life Subsea Gas Condensate System Using Historical Field Data

2021 ◽  
Author(s):  
Lawrence Khin Leong Lau ◽  
Kun An ◽  
Wu Jun Tong ◽  
Song Wang ◽  
Zhi Wei Yue ◽  
...  
Keyword(s):  
Field Data ◽  
Pressure Fluctuations ◽  
Flow Characteristics ◽  
Late Life ◽  
Flow Regimes ◽  
Gas Condensate ◽  
Well Production ◽  
Water Cut ◽  
Instrumentation Failure ◽  
Condensate System

Abstract Depleting reservoir pressure, increasing water cut and decreasing overall system production leading to increased liquid holdup are among the key challenges for typical late life gas condensate production system. This paper elucidates modelling details of a late life offshore subsea gas condensate system and how the findings are implemented and validated with actual field data for successful outcomes. There is only one subsea well remain in operation with relatively long subsea flowlines. Subsea pressure and temperature transducers are out of service as the asset approaches the end of design life. In this context, flow assurance team has taken the modelling approach in order to minimize cost and to maximize values. Detailed transient multiphase thermohydraulics models are developed and benchmarked against field data. Historical field data over the past two years are utilized in order to predict the trend for key parameters such as well production rates and water to gas ratio (WGR). Matrix of simulation including the predictions of slugging flow regimes are carried out for the entire flow path, from reservoir characteristics descriptions at bottom hole, through flow regimes analysis at topsides slug catcher. Three categories of operation characteristics, namely the low risk, medium risk, and high risk production periods are identified. It is predicted that the system would start to fall into slugging flow regimes from 2 months onwards with final production end date of after 10 months. This is shared with wider team such that operations and base management teams are informed with predicted multiphase flow characteristics for the remaining production life. As such, gas supply succession plan can be executed in time to ensure uninterrupted downstream commercial agreement. Feedbacks from operations team revealed accurate predictions of such analysis, including slugging flow phenomenon which was associated with flow and pressure fluctuations, was observed in field as predicted by the study. More importantly, the production cut-off date is accurately predicted 10 months ahead and within the accuracy of ± 1 week. This study demonstrated how historical field data, coupled with detailed transient multiphase thermohydraulics modelling, can be utilized for offshore gas condensate production predictions during late life. Without transducers and/or virtual metering data feed, production end date can be accurately predicted based on key parameters analysis. This is particularly valuable for supply succession planning and is deemed a successful case study with significant positive outcomes which can be used as reference for other gas condensate assets.

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.

A Comprehensive Machine Learning Approach for Quantitatively Analyzing Development Performance and Optimization for a Heterogeneous Carbonate Reservoir in Middle East

2021 ◽  
Author(s):  
Ruijie Huang ◽  
Chenji Wei ◽  
Baohua Wang ◽  
Baozhu Li ◽  
Jian Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Principal Component ◽  
Optimal Number ◽  
Gas Production ◽  
Carbonate Reservoir ◽  
Multidimensional Data ◽  
Well Performance ◽  
Reference Case ◽  
Evaluation Approach ◽  
Water Cut

Abstract Compared with conventional reservoir, the development efficiency of the carbonate reservoir is lower, because of the strong heterogeneity and complicated reservoir structure. How to accurately and quantitatively analyze development performance is critical to understand challenges faced, and to propose optimization plans to improve recovery. In the study, we develop a workflow to evaluate similarities and difference of well performance based on Machine Learning methods. A comprehensive Machine Learning evaluation approach for well performance is established by utilizing Principal Component Analysis (PCA) in combination with K-Means clustering. The multidimensional dataset used for analysis consists of over 15 years dynamic surveillance data of producers and static geology parameters of formation, such as oil/water/gas production, GOR, water cut (WC), porosity, permeability, thickness, and depth. This approach divides multidimensional data into several clusters by PCA and K-Means, and quantitatively evaluate the well performance based on clustering results. The approach is successfully developed to visualize (dis)similarities among dynamic and static data of heterogeneous carbonate reservoir, the optimal number of clusters of 27-dimension data is 4. This method provides a systematic framework for visually and quantitatively analyzing and evaluating the development performance of production wells. Reservoir engineers can efficiently propose targeted optimization measures based on the analysis results. This paper offers a reference case for well performance clustering and quantitative analysis and proposing optimization plans that will help engineers make better decision in similar situation.

Validation Techniques for 6-DOF Vibration Data Acquisition

2012 ◽  
Vol 55 (1) ◽  
pp. 10-24 ◽  
Author(s):  
Michael Hale ◽  
Jesse Porter
Keyword(s):  
Vibration Control ◽  
Control Systems ◽  
Field Data ◽  
Dynamic Test ◽  
Degree Of Freedom ◽  
Data Validation ◽  
Standard Equipment ◽  
Vibration Data ◽  
Mdof Systems

Multiple Degree of Freedom (MDOF) excitation systems and MDOF vibration control systems continue to improve, and are now standard equipment in many dynamic test laboratories. Determination of an input specification for such MDOF systems is critically dependent on properly acquired field data. Validation of field data will be discussed and demonstrated employing the same transformation tools used in both transformation-based 6-degree-of-freedom (6-DOF) vibration control and generalized MDOF vibration specification development (VSD).

Oil-Rate Forecast by Inferring Fractional-Flow Models From Field Data With Koval Method Combined With the Capacitance/Resistance Model

2015 ◽  
Vol 18 (04) ◽  
pp. 534-553 ◽  
Author(s):  
Fei Cao ◽  
Haishan Luo ◽  
Larry W. Lake
Keyword(s):  
Field Data ◽  
Oil Production ◽  
Data Driven ◽  
Analytical Models ◽  
Fractional Flow ◽  
Physical Behavior ◽  
Flow Models ◽  
Drainage Volume ◽  
Resistance Model ◽  
Water Cut

Summary Many empirical and analytical models were developed to forecast oil production. Empirical models (including data-driven models) can, for example, find correlations between oil cut and production, but they lack explicit knowledge of the physical behavior. Classic analytical models are loyal to reservoir physics. Nevertheless, they often require estimation of water saturation as a function of time, which is difficult to obtain for multiwell reservoirs. It is desirable to combine advantages of both empirical and analytical models and develop a physical-model-based method that uses field data to infer oil rate. In this paper, we propose to infer fractional-flow models from field data by use of the Koval (1963) theory. We inversely solved the Koval fractional-flow equation to obtain a relationship between water cut and dimensionless time. By history matching field water-cut data, two model parameters, the Koval factor and the producer-drainage volume, are estimated. Nevertheless, it is challenging to use the Koval approach as a predictive model directly because the injection contribution into each producer in a future-time horizon must be evaluated first. To address this issue, we combine the Koval approach with the capacitance/resistance model (CRM), which characterizes the injector/producer connectivities and response time. The material balance of fluids is established in a producer-based drainage volume to consider the contributions from nearby injectors and the time lag in production caused by reservoir/fluids compressibility. A regression approach is simultaneously advanced to minimize the model error. Because of robustly integrating the reservoir physical behavior and the data-driven approach, the combination of the Koval theory and the CRM can result in a synergy that leads to accurate oil-rate predictions. We validated this integrated method in synthetic homogeneous and heterogeneous reservoirs to test its reliability, and further applied it to a field case in western Venezuela. Case studies demonstrate that one can use this integrated model as a real-time tool to characterize interwell connection and to predict future oil production accurately.

A New Model for Evaluation of Horizontal Well in Bottom Water Reservoirs

2013 ◽  
Vol 411-414 ◽  
pp. 486-491
Author(s):  
Yue Dong Yao ◽  
Yun Ting Li ◽  
Yuan Gang Wang ◽  
Ze Min Ji
Keyword(s):  
Oil Recovery ◽  
Field Data ◽  
Horizontal Well ◽  
Bottom Water ◽  
Interaction Analysis ◽  
Evaluation Model ◽  
Water Reservoir ◽  
Well Performance ◽  
Water Reservoirs ◽  
Actual Field

It is the aim of this research to describe the horizontal well performance in different conditions, this paper firstly introduces 13 dimensionless variables to describe the influence factors of horizontal well performance in bottom water reservoir and calculates the range of all the variations from low to high level by making a statistics of the actual field data of the 23 horizontal wells, then establishes the oil recovery model with response surface method using a 3 level-13 variables Box-Behnken design (BBD) . Based on the evaluation model, single factor sensitivity and interaction analysis between any two factors are carried out. Finally, research on horizontal well in typical bottom water reservoirs indicates that the values calculated by the new evaluation model fit the actual field data, which proves that the evaluation model can provide criteria for the design or optimization of horizontal well development in a bottom water reservoir.

A Novel Multi-Layer Intelligent Test and Adjustment Technology for Water Injection Well

2012 ◽  
Vol 424-425 ◽  
pp. 732-736 ◽  
Author(s):  
De Li Jia ◽  
Feng Shan Wang ◽  
Shu Jin Zhang
Keyword(s):  
Water Injection ◽  
Dynamic Test ◽  
High Water ◽  
Adjustment Process ◽  
Flow Adjustment ◽  
High Water Cut ◽  
Water Cut ◽  
Injection Effect ◽  
Injection Technology ◽  
Clock Calibration

The layered recovery technology has been applied to the heterogeneous multi-layer standstone oilfields for many years. However, as these standstone oilfields have been entering into the ultra-high water cut period, the conventional layered water injection technology has too long test and adjustment period and heavy workload and cannot determine the reservoir condition. To solve this problem, this paper proposes and develops an intelligent multi-layer water injection technology suitable for the ultra-high water cut period based on the synchronous dynamic test and adjustment idea. The whole flow adjustment process has no any intervention and the synchronous dynamic flow adjustment of each layer finishes by the digital clock calibration thereby avoiding interlayer interference. This technology also can obtain data by the redisplay of computer. The experimental results show that this technology not only improves water injection effect and reduces the field workload, but also provides the basis of data analysis for implementation and adjustment of meticulous oil development plan

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