Enhanced Back Allocation Process Assisted by Real Time AI-Based Virtual Metering. Field Case Study in a Giant Field Project
Authors: Rubio Erismar , Reddicharla Nagaraju 
Published: October 2023

Abstract
Artificial Intelligence (AI) is relatively new neurocomputational science employed in oil industry to solve wide spectrum of non-linear problems with high parallelism, fault and noise tolerance. AI seems very attractive for its remarkable capabilities of processing-correlating data and learning attributes. This paper briefs a successful implementation of an ecosystem between AI and physics-based models in a smart field, that support accurate well allocation process during the project start-up when testing facilities were not available.
This case study is presented in a smart field during the commissioning of a new phase-project incorporating 45 new strings at a time. Reconciliation between wells and fiscal meters vanished after incorporating the newly commissioned wells with no flow-tests. An innovative AI-model was developed to estimate well rates from real-time surface parameters (pressure, temperature, choke &gas lift rate) to assist the allocation process for those strings, utilizing extensive well test history and subsurface data from pre-existing nearby wells. Data mining and physics-based models were integrated to develop an ecosystem that can virtually measure daily oil, water and gas rates with reasonable accuracy enhancing well back-allocation process.
Initial newly-strings models were built using only one commissioning test, consisting of diverting wells to a portable separator for few hours. This practice usually overestimates the productivity index since rates are captured in transient mode. Later, when wells are flowing for longer and cleaned up, production rates decrease after stabilization. Fortunately, these changes of performance over time are well-captured by the real time parameters. Therefore, this paper proposal can be extended to other digital fields to track the well performance and minimize the error in back allocated rates as it was observed in this field application, demonstrating how reconciliation factor was continuously enhanced day after day, once AI-virtual rate prediction was introduced. AI-model prediction was later verified against actual flow tests via portable separators with an average error below 20%. Virtual rates were predicted using machine learning (ML) techniques multilinear regression, Artificial Neural-Network (ANN) and Self-Organizing Maps (SOM). The results were used to update models for better well performance prediction. The performance of these ML techniques were improved with intelligent inputs and proper segregated and intuitive training to machine.
As a practical application, an ecosystem was developed combining AI and physics to predict well-performance of newly-drill wells under gas lift and natural flow, using real-time measurement and the digital framework, to provides reasonably accurate in simulating both training and test time-dependent well performance inferred from pressure, temperature, gas-lift rate and choke. The novelty of this paper consists in the developed data cleansing and association technics that enable the implementation of successful AI-models in wells with no flow test data for training, levering Real-Time data usage.