RasGas Experience with Production Optimisation System, A Success Story
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A real-time production surveillance and optimization system has been developed to integrate available surveillance data with the objective of driving routine production optimization. The system aims to streamline data capture, automate data quality assurance, integrate high and low frequency data to extract maximum value, optimize the design and analysis of commingled well tests, and provide real-time multi-phase well rate estimates for continuous well performance evaluation. A key challenge identified was the need to understand individual well contribution during commingled well tests, as traditional approaches may provide unrepresentative results. Additionally, the well tests are typically infrequent, thus further limiting the reliability of estimated well rates as production system dynamics between well tests are not accounted for. A third challenge recognized was the need for efficient testing procedures in order to minimize deferred production. To address these issues, a fully integrated model of the production system was used, and is driven by a computational algorithm that automatically calibrates the model to real-time sensor data. A new systematic approach was developed to analyze multi-segment commingled well tests simultaneously to improve the accuracy of resulting measurements. Between well tests, a robust regression algorithm is used to continuously adapt and re-calibrate the model when well conditions change. This algorithm can automatically detect sensor bias and apply an appropriate weighting when calibrating the model. In addition, a regularization technique is also used to prevent physically unrealistic changes in the well parameters between infrequent well tests. The technology is currently applied to an offshore deepwater asset and early benefits include a 2% production uplift realized from optimizing gas lift allocation and performing a single well routing change recommended by the technology. Furthermore, more reliable rate allocation to wells has improved the quality of subsurface models used for reservoir management.
A real-time production surveillance and optimization system has been developed to integrate available surveillance data with the objective of driving routine production optimization. The system aims to streamline data capture, automate data quality assurance, integrate high and low frequency data to extract maximum value, optimize the design and analysis of commingled well tests, and provide real-time multi-phase well rate estimates for continuous well performance evaluation. A key challenge identified was the need to understand individual well contribution during commingled well tests, as traditional approaches may provide unrepresentative results. Additionally, the well tests are typically infrequent, thus further limiting the reliability of estimated well rates as production system dynamics between well tests are not accounted for. A third challenge recognized was the need for efficient testing procedures in order to minimize deferred production. To address these issues, a fully integrated model of the production system was used, and is driven by a computational algorithm that automatically calibrates the model to real-time sensor data. A new systematic approach was developed to analyze multi-segment commingled well tests simultaneously to improve the accuracy of resulting measurements. Between well tests, a robust regression algorithm is used to continuously adapt and re-calibrate the model when well conditions change. This algorithm can automatically detect sensor bias and apply an appropriate weighting when calibrating the model. In addition, a regularization technique is also used to prevent physically unrealistic changes in the well parameters between infrequent well tests. The technology is currently applied to an offshore deepwater asset and early benefits include a 2% production uplift realized from optimizing gas lift allocation and performing a single well routing change recommended by the technology. Furthermore, more reliable rate allocation to wells has improved the quality of subsurface models used for reservoir management.
Historically well production flow surveillance has been attracting attention from reservoir engineers. The accurate estimation of individual well contribution, Has profound impact on reservoir management. Often production allocation suffers from multiple issues such as quality of available production data and production rate estimation in case of inaccurate and uneconomical measurements. These issues can be mitigated if the well has accurate, calibrated Multiphase Flow Meters (MPFMs) and Virtual Flow Meters (VFMs). Reliability of digital meters is primarily dependent on regular well testing to keep the model updated and tuned to latest reservoir characteristics. As an industry wide common practice, well tests are performed in a timely manner to ensure to allocate production accurately to each well. But performing solo well tests for subsea wells is not trivial as it may seem. Differed production due to downtime associated with flowing subsea wells to a test separator and inability of mature wells to flow to the test separator result in additional challenges. Under such situations, commingled well tests are performed to estimate well rates. Individual well rates can be estimated using test by difference (TBD) method. The apparent simplicity of the TBD method often results in incorrect estimation of allocation rates as the test conditions and production conditions differs significantly. As a result, the credibility of the forecast and the ability to optimize production are compromised. In this paper a novel application called Commingled Well Test Analysis (CWTA) is reviewed which addresses these aforementioned issues. This integrated application comprises of data loading interface, data visualization interface, core optimization engine and an interactive user interface. To demonstrate the capability, three wells from a deep water conventional wells are analyzed using commingled well tests and well performance parameters (PI, WCUT and GOR) are obtained. Based on the tuned parameters, well rates are predicted and compared against rates obtained from sales meter and MPFM meters. As per the comparison, well allocation rates are validated and health of MPFM meters are assessed. As an additional benefit, proper designing of commingled well tests enable in 25-30% time savings of well testing.
Estimation of Water Cut, Gas Oil Ratio and Inflow Parameters for Deep Water Wells with Infrequent Well Tests Using Regression-Based Method
Real-time production surveillance and optimization requires availability of predictive well models with reasonable accuracy that can estimate production rates in response to change in operating conditions. These models that are mostly physics based are parameterized in terms of gas oil ratio (GOR), water cut (WC), productivity index (PI), and reservoir pressure. Further, the models are tuned using measured data namely production rates of oil, water, gas, and pressure measurements such as downhole gauge pressure, wellhead and flowline pressures that are captured during a well test. For mature oil fields where, well tests are infrequent or sensors start malfunctioning, relevant data required for model tuning is no longer available. In absence of updated models, real-time predictions tend to deviate with time compared to the observed data resulting in less reliable well rate allocation and production optimization recommendations, if any. This work describes a regression-based technique and its application for predicting quantitative as well as directional changes in well parameters between well tests that can be used to improve well allocations. A regression method that estimates well parameters while minimizing a non-linear least square loss function derived from deviation between measured and model-based estimates of rate and pressure data is implemented. The method can estimate well parameters for both an individual and multiple wells simultaneously while solving for a network of wells connected to production separator using IPM GAP. The application of the method to a subsea asset is demonstrated while evaluating its performance for different scenarios comprising variation in number of wells and well parameters. Additionally, the capability of the method to predict directional changes in well parameters is demonstrated by validating it against historical data. The estimates from regression method were found within 5% difference compared to well parameters obtained from multiphase flow meters for the scenarios where number of wells and the number of regression parameters per well were limited. This difference increased with increase in number of wells and number of regression parameters per well owing to the fact that solution space expands with increased degrees of freedom. However, the directional changes in parameters were predicted accurately when looked at larger time scales. It was inferred that the application of regression method is best suited for the scenarios where well parameter estimations are needed for a limited number of wells and the parameters for the remaining wells in a network are representative. Additionally, availability and reliability of sensor data largely impacts the method outcomes.
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