No abstract

Gas injection methods such as Continuous Gas Injection, Water Alternating Gas and conventional Gas-Assisted Gravity Drainage (GAGD) have been widely used to improve/enhance oil recovery for conventional oil reservoirs. However, applications to naturally fractured basement reservoirs are still limited. This paper will introduce a case study of a new and effective GAGD method conducted in a Huff ‘n’ Puff fashion to improve oil recovery for a fractured basement reservoir in Cuu Long Basin, offshore Vietnam. A GAGD pilot, which consists of 4 cycles, was conducted in which dry gas was periodically injected into an existing production well in an isolated area. It was expected that as the injected gas rose to the top to form a gas zone, it would push the Gas Oil Contact (GOC) downwards and might also push the Water Oil Contact (WOC) to the lower part of the producer or even away from the bottom of its wellbore. Before commencing the 4 cycles, gas injection, asphalting and reservoir simulation studies were conducted. In addition, a thorough forward plan was carefully devised before each cycle to determine possible effects of important operating parameters to the final outcome of that cycle. From the results of the 4 cycles, it could be concluded that the gas injection volume is well correlated with cumulative water-free oil production, a parameter which indicates the effectiveness of the method in terms of the gravity-drainage mechanism. It could also be found that the final incremental oil gain of each cycle depended upon, not only, the gas injection volume, but also other important factors such as gas injection, shut-in time… suggesting that a non-linear optimization exercise is necessary to make the whole pilot economically successful. The success of the GAGD pilot proves that it could be a simple and effective Improved Oil Recovery (IOR) method for fractured basement reservoirs. That can be a foundation for further application of the method to other reservoirs in the Cuu Long Basin.

This case study investigated the effects of formation reservoir properties, aquifer influx, and production scheme on ultimate recovery and production behaviors of a gas-condensate sandstone reservoir Sand20 offshore Vietnam. Optimum production strategy was then formulated to maximize the hydrocarbon recovery while reducing the water treatment cost. The approach focused on the construction of benchmarked radial numerical models to describe the water coning and breakthrough phenomenon and to better understand the impacts of aquifer on deliverability and ultimate recovery of a gas-condensate reservoir. In this study, all factors that have potential impacts on gas and oil ultimate recoveries such as gas production rate, completion length, aquifer size, reservoir horizontal permeability, and permeability anisotropy were investigated. The numerical results showed that for permeability greater than 100 mD, withdrawal rates do not have significant impacts on reservoir gas recovery, while the oil recovery decreases with increasing withdrawal rates. To maximize the ultimate oil recovery, minimize total water production, delay water breakthrough time, and prolong field production life, the wells are recommended to produce at a reasonable low gas flow rate. On the other hand, a minimum gas production rate is required to recover all the reserves to meet the field's production strategy. Aquifer size was found to have no impact on water breakthrough time for this gascondensate reservoir, but it can have big impact on the recovery factor and the total water production. This study also suggested that perforation interval should be sufficiently long to maximize recovery. Finally, it was found that water-gas ratio does not increase rapidly until approximately 90% of perforation interval is flooded with water.

This study aims to apply machine learning (ML) to make history matching (HM) process easier, faster, more accurate, and more reliable by determining whether Local Grid Refinement (LGR) with transmissibility multiplier is needed to history match gas-condensate wells producing from geologically complex reservoirs and determining how LGR should be set up to successfully history match those production wells. The main challenges for HM gas-condensate production from Hai Thach wells are large effect of condensate banking (condensate blockage), flow baffles by the sub-seismic fault network, complex reservoir distribution and connectivity, highly uncertain HIIP, and lack of PVT information for most reservoirs. In this study, ML was applied to analyze production data using synthetic samples generated by a very large number of compositional sector models so that the need for LGR could be identified before the HM process and the required LGR setup could also be determined. The proposed method helped provide better models in a much shorter time, and improved the efficiency and reliability of the dynamic modeling process. 500+ synthetic samples were generated using compositional sector models and divided into training and test sets. Supervised classification algorithms including logistic regression, Gaussian, Bernoulli, and multinomial Naïve Bayes, linear discriminant analysis, support vector machine, K-nearest neighbors, and Decision Tree as well as ANN were applied to the data sets to determine the need for using LGR in HM. The best algorithm was found to be the Decision Tree classifier, with 100% and 99% accuracy on the training and the test sets, respectively. The size of the LGR area could also be determined reasonably well at 89% and 87% accuracy on the training and the test sets, respectively. The range of the transmissibility multiplier could also be determined reasonably well at 97% and 91% accuracy on the training and the test sets, respectively. Moreover, the ML model was validated using actual production and HM data. A new method of applying ML in dynamic modeling and HM of challenging gas-condensate wells in geologically complex reservoirs has been successfully applied to the high-pressure high-temperature Hai Thach field offshore Vietnam. The proposed method helped reduce many trial and error simulation runs and provide better and more reliable dynamic models.

This paper reports a successful case study of applying machine learning to improve the history matching process, making it easier, less time-consuming, and more accurate, by determining whether Local Grid Refinement (LGR) with transmissibility multiplier is needed to history match gas-condensate wells producing from geologically complex reservoirs as well as determining the required LGR setup to history match those gas-condensate producers. History matching Hai Thach gas-condensate production wells is extremely challenging due to the combined effect of condensate banking, sub-seismic fault network, complex reservoir distribution and connectivity, uncertain HIIP, and lack of PVT data for most reservoirs. In fact, for some wells, many trial simulation runs were conducted before it became clear that LGR with transmissibility multiplier was required to obtain good history matching. In order to minimize this time-consuming trial-and-error process, machine learning was applied in this study to analyze production data using synthetic samples generated by a very large number of compositional sector models so that the need for LGR could be identified before the history matching process begins. Furthermore, machine learning application could also determine the required LGR setup. The method helped provide better models in a much shorter time, and greatly improved the efficiency and reliability of the dynamic modeling process. More than 500 synthetic samples were generated using compositional sector models and divided into separate training and test sets. Multiple classification algorithms such as logistic regression, Gaussian Naive Bayes, Bernoulli Naive Bayes, multinomial Naive Bayes, linear discriminant analysis, support vector machine, K-nearest neighbors, and Decision Tree as well as artificial neural networks were applied to predict whether LGR was used in the sector models. The best algorithm was found to be the Decision Tree classifier, with 100% accuracy on the training set and 99% accuracy on the test set. The LGR setup (size of LGR area and range of transmissibility multiplier) was also predicted best by the Decision Tree classifier with 91% accuracy on the training set and 88% accuracy on the test set. The machine learning model was validated using actual production data and the dynamic models of history-matched wells. Finally, using the machine learning prediction on wells with poor history matching results, their dynamic models were updated and significantly improved.