Sand management is one of the key component of Bongkot production processes. Current sand production prediction is based on a model which requires sonic and density logs for all the wells. However, a combination of complex well architecture and focus on reducing well cost resulted in many wells not having acquired these important logs. This project has implemented new technique of "Artificial Neural Network" to solve this problem. Using this method, synthetic logs are generated to obtain the values of missing sonic and density data. These data are then used in the existing sand models to predict sand production potential. This project was evaluated with three field cases. The sand failure predictions based on synthetic rock properties matched with actual sand production. Therefore, the sand prediction workflow has been updated to include log synthetic if acroustic or density log are missing.
Seismic well tie is a critical process to verify the time-depth relationship of a well. This process requires density and sonic transit time data. However, sonic logs are usually not acquired due to cost saving, unfavorable well path, or other operational issues. Attempts to generate synthetic logs by Gardner equation, porosity correlation, or depth correlation did not provide the required accuracy. Therefore, the goal of our project was to generate synthetic sonic logs using machine learning technique for seismic well ties. This paper will compare the different methods tested, compare the results and lists the advantages of using Machine Learning. This approach uses machine learning technique to create synthetic sonic logs. The machine learning model is trained to predict sonic log from other relevant logs. The model representativeness is confirmed by blind tests, which consists of two steps. The first step compares the synthetic sonic logs to the actual sonic logs. In the second step, four synthetic seismograms are generated from actual sonic, machine learning synthetic sonic, Gardner predicted sonic, and averaged constant sonic. The seismic well ties are compared between those four synthetic seismograms. Once the machine learning synthetic and actual logs show similar results, the model is deemed good and can be applied on wells that do not have sonic logs. The synthetic seismograms are then generated using synthetic sonic logs for all the wells that do not have actual sonic logs. The use of synthetic sonic logs gives us the ability to Generate synthetic seismogram to tie wells that do not have sonic dataReduce the number sonic data acquisition, saving time and moneyReduce the risk of long logging string getting stuck in the hole that would requires fishing operations and its associated cost.
In Pru Kratium (PKM) highly viscous oil reservoir, the secondary recovery method such as waterflood can adversely affect production due to an unfavorable mobility ratio, resulting in viscous fingering and early water breakthrough. Although polymer flooding which usually mitigates unfavorable mobility ratio, this technique also faces a challenge due to the high salinity formation water which may lead to excessive polymer consumption. To improve recovery, a special polymer injection scheme was designed and the small-scale pilot was initiated. After one year of injection, the pilot demonstrated that the polymer improved the sweep efficiency of the flood in both vertical and horizontal directions. Ultimately, the increase in oil recovery factor gain was 2.41%.
Field A is an onshore oil field in Thailand. This area contains biodegraded medium-heavy crude reservoir; 19°API oil gravity and 144 cp viscosity. Therefore, the field suffers from a low recovery factor due to high crude viscosity. On one hand, bacteria have exerted an adverse effect on production, on the other hand, it means that the condition of the reservoir is suitable for implementing Microbial Enhanced Oil Recovery (MEOR). The MEOR is a technology that utilizes microorganisms (mainly bacteria), to enhance oil production, especially for medium-heavy oil. By feeding nutrients to bacteria, several metabolites were produced that would be useful for oil recovery. This technique is well known for its low investment cost, hence, high return. The technical screening confirmed that the reservoir and fluid properties are suitable for MEOR. Consequently, sixteen core samples and three water samples were collected for indigenous bacteria analysis. Although the laboratory indicated there are countless bacterial strains in the reservoir, the nitrate-reducing biosurfactant-producing bacteria group was identified. This bacteria group belongs to the Bacillus genus which produced biosurfactant and reduced crude viscosity by long-chain hydrocarbon degradation. Therefore, the treatment design aimed to promote the growth of favorable bacteria and inhibit undesirable ones. Consequently, a combination of KNO3 and KH2PO4 solutions and a specialized injection scheme was tailored for this campaign. The pilot consisted of two candidates those were well W1 (76% water cut), and well W2 (100% water cut). The campaign was categorized into three phases, namely, 1.) baseline phase, 2.) injection and soaking phase, and 3.) production phase. Firstly, the baseline production trends of candidates were established. Secondly, KNO3 and KH2PO4 solutions were injected for one month then the wells were shut-in for another month. Lastly, the pilot wells were allowed to produce for six months to evaluate the results. The dead oil viscosity of well W1 was reduced from 144 cp to 72 cp which led to a 6.44 MSTB EUR gain or 1.3% RF improvement. On the other hand, the productivity of well W2, the well with 100% water cut, was not improved. This was expected due to insufficient in-situ oil saturation for a bacteria carbon source. Considering the operational aspect, there was no corrosion issue or artificial lift gas-lock problem during the pilot.
Greater Bongkot North is a gas field located in Gulf of Thailand and on production since 1993. Most of the old wellhead platforms (30%) lack remote well test facilities which requires personnel visits for any well test measurement. Often, well testing in these platforms get lower priority compared to other operations in a matured field. This project implemented artificial intelligent (AI) technique to estimate gas rate from other available engineering and geological parameters. A new approach using machine learning was applied to estimate gas production rate where actual measurements are not available. Actual production well test data was used to train the model. Input parameters used were: Surface facility information Fluid properties Production condition Geological setup A blind test on the subset of historical data showed a level of confidence (R2) value of 0.93. This provided confidence to proceed with a full field pilot. A pilot was conducted during January to May 2018. The area of pilot was spread across various geological, operating and surface condition setups to reduce sampling bias. The pilot demonstrated the following use cases: Improved prediction accuracy in wells with no recent test, achieving primary object of model. Detection of well behavior changes: The model could detect changes in well behavior without human intervention much before the trends become obvious for engineers to detect. Improved potential estimation in wells with leaks in wellhead chokes where conventional analysis followed in Bongkot is not possible due to improper wellhead shut-in pressure measurement. Improved efficiency with production allocation: The conventional method requires significant time (40-80 person hours per month) to make the data available for production allocation. This can be shortened significantly by use of this method In essence, this project demonstrated the potential use of artificial intelligent to improve efficiency in a matured gas field operating under marginal conditions.
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