Wintershall is conducting a technology project for development and field application of MEOR (Microbiologically Enhanced Oil Recovery) in collaboration with BASF. The successful results of the laboratory phase led to a first small confined pilot Huff'n’ Puff (HnP) in a Wintershall mature oil field to prove that the laboratory-developed concept works in the field under reservoir conditions. A suitable well for the MEOR operation was selected in the studied field based on selection criteria. The selected well is a former producer approximately 900 m deep. After a USIT run it was decided to recomplete it. Prior to MEOR HnP pilot, an injectivity test was performed to allow for re-assessment of the current petrophysical and geological properties around the well. In order to establish the baseline for the pilot evaluation, a comprehensive monitoring program consisting of microbiological, chemical and petrophysical surveys commenced just after the well recompletion. The surface set-up designed for follow-up MEOR field operations was installed in the field. The mixing of the MEOR solution with the injection water was regulated automatically by measuring the injection rate. The injection took four days, followed by an incubation period of five weeks. During the nutrient injection, the injectivity was significantly lower than the one obtained from a previous injectivity test. As a result, the total volume of injected nutrient was lower than initially planned. Nevertheless, the volume was sufficient to achieve the pilot objectives. The injection was carried out under matrix conditions by keeping the pressure below the fracture pressure. The injected fluid temperature was somewhat lower than planned, but according to downhole measurements, still high enough for microbial growth. It was observed that there was an oxygen ingress into the system through the injection pump, however no detrimental effect was seen on microbial activity. After the shut in period, a comparable volume of the injection fluid was produced back. The tracer concentration in the back produced fluid was used to calibrate the chemical and microbiological effects of MEOR.

The paper presents laboratory testing approach to evaluate DIF (Drill-In-Fluid) damage and wellbore cleanup effectiveness in open hole horizontal producer wells. It also investigates the fine movement damage and also the side effect of mud cake removal if it is left in wellbore for long period. The formation damage tests were carried out on sandstone core plugs from 3 different formations with permeability varying from several milidarcy to hundreds milidarcy. The WBM (Water Base Mud) was used with mostly consisted of CaCO3, NaCl, and KCl. Prior to formation damage test, critical velocity test was performed to investigate the fine movement damage. The formation damage tests were started by circulating the mud with 300 - 400 psi overbalance across the core plugs to create mud cake. The mud was then displaced by brine 8.8 ppg and high viscosity fluid was circulated to simulate the real condition in field for hole cleaning process. Several mud cake removals were then circulated and soaked for 1 and 5 days in the system. Finally, the oil return permeability was measured and compared with initial permeability. Throughout the study, it is observed that the critical velocity is mainly controlled by the clay content. Porosity and permeability also plays significant role in the plugging behavior due to fine migration. Acid based mud cake removal can enhance the formation permeability if it is allowed to leak-off through the rock matrix. It also depends on the mineral content of the formation (acid soluble minerals). Delayed acid based mud cake removal outperformed other mud cake removals in this study. The most optimum soaking time using the delayed acid based mud cake removal in this study is 1 day. However, there is no permeability impairment identified if the mud cake removal is left up to 5 days.

The question of how to safeguard well integrity is one of the most important problems faced by oil and gas companies today. With the rise of Artificial Intelligence (AI) and Machine Learning (ML), many companies explore new technologies to improve well integrity and avoid catastrophic events. This paper presents the Proof of Concept (PoC) of an AI-based well integrity monitoring solution for gas lift, natural flow, and water injector wells. AI model prototypes were built to detect annulus leakage as incident-relevant anomalies from time series sensor data. The historical well annulus leakage incidents were classified based on well type and the incident relevant anomalies were categorized as short and long-term. The objective of the PoC is to build generalized AI models that could detect historical events and future events in unseen wells. The success criteria are discussed and agreed with the Subject Matter Experts (SMEs). Two statistical metrics were defined (Detected Event Rate – DER – and False Alarm Rate – FAR) to quantitively evaluate the model performance and decide if it could be used for the next phase. The high frequency sensor data were retrieved from the production historian. The importance of the sensor was aligned with the SMEs and only a small number of sensors was used as input variable. The raw data was pre-processed and resampled to improve model performance and increase computational efficiency. Throughout the PoC, the authors learnt that specific AI models needed to be implemented for different well types as generalization across well types could not be achieved. Depending on the number of available labels in the training dataset, either unsupervised or supervised ML models were developed. Deep learning models, based on LSTM (Long-Short Term Memory) autoencoder and classifier were used to detect complex anomalies. In cases where limited data were available and simplistic anomaly patterns were present, deterministic rules were implemented to detect well integrity-relevant incidents. The LIME (Local Interpretable Model-Agnostic Explanations) framework was used to derive the most important sensors causing the anomaly prediction to enable the users to critically validate the AI suggestion. The AI models for gas lift and natural flow wells achieved a sufficient level of performance with a minimum of 75% of historical events detected and less than one false positive per month per well.

The paper presents evaluation of passive ultrasonic logging tool deployed using E-line to assess casing integrity in 3 oil producing wells in South of Oman. In addition a comparison is offered between the ultrasound logging technology and a more conventional well integrity test using a hoist and multi-set plug that has been utilized to date. The ultrasonic log was run in a selection of oil producing wells, operated by beam pump. Those wells were confirmed as having well integrity issues from well surveillance data and were causing significant oil deferment (2.2% of total oil production). Those wells have a history of cementation challenges owning to heavy losses that occur within a water bearing zone located above the pay zone. This, combined with the presence of H2S and oxygenated water at this depth, has resulted in a number of corrosion related integrity issues across the field. The logging program was originally planned inside tubing with surface pressures of 1,800 psi but it was decided to log inside casing (without tubing) because leak was more severe than predicted (1 m3/min of leak rate). In wells A and B, leak points at 630 m and 225 m were identified and respectively verified. However, another leaking interval in well A was also identified from conventional WIT using hoist. In well C, no pressure held on surface causing lack of differential across leak which resulted in identification failure on first attempt (inside tubing). On second attempt (inside casing), unclear ultrasound reading was attained but seven leak points still can be identified after several log passes. The tool can save significant hoist time and will become viable alternative. In conclusion this paper illustrates examples of where the ultrasonic log has provided highly accurate leak detection, significant time saving and improvements in overall operating efficiency. The limits of the technology are also discussed with recommendations provided for the application of the service based on operational experience gained during the technical evaluation.

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