Most of the northern Oman fields have tight carbonate reservoirs. The field under study was initially produced under natural depletion with declining reservoir pressures through vertical and horizontal wells (1990-2002), then followed by two years water flood piloting and thereafter full field line drive water flood implementation through open hole horizontal wells. After more than 10 years of water injection, water production increase is seen in most of the wells and water cut reached an average of 65-75% in the major producing blocks. Based on current data, the ultimate recovery factor of the current water flood development from these blocks is expected to be as high as 40-45%. In sight of the continuous increase of water production from the field and taking into account that more than 50% of the field's oil initially in place will not be recovered by the current secondary production mechanism (water flood), the block operator initiated research work on the tertiary production mechanism to maximize the oil recovery from the field. Extensive laboratory and field testing works were performed over the past five years to select the suitable and optimum IOR/EOR technique to be implemented in this tight carbonate reservoir. The process started with screening of different EOR methods and was followed by laboratory fluid behavior testing and core flooding experiments for the selected method. Out of all EOR methods, chemical EOR was screened as the most convenient and applicable method to be implemented due to the nature of both reservoir and fluid. This paper summarizes the working process which was followed to eventually select the convenient chemical starting from screening process, then laboratory work, followed by single well field testing and eventually extended injection field testing. High level results will be presented for the first three milestones and more elaborations on the extended injection field testing will be presented. Results for both field trials; the huff and puff and the extended injection are encouraging with incremental oil gains exceeding the expectation from these trials. The extended chemical injection field trial was executed in ~ 40 acre, horizontal line drive pattern utilizing two horizontal injectors and four horizontal producers with two vertical pressure observation wells. The evaluation of injection results was based on actual daily production and injection data as well as reservoir log and core data collected before chemical injection. Comparing to water flood, initial recovery factor evaluation indicate possible improvement of up to 18% (per pore volume injected) in unit A which has more mature water flood where water cut exceeding 80% but less oil volume. Recovery improvement in unit B, a less mature water flood reservoir unit, was not remarkable. Post job analysis and review claims this due to the relatively immature water injection and thus lower water cut in this reservoir unit. Unit B is also three times thicker than unit A, which meant it received a lower chemical volume, which might have resulted in a lower recovery performance. With limited field trials of surfactant injection in tight carbonate reservoirs in Middle East, this case study will help to enrich the literature with actual field data of continues surfactant injection in tight carbonate field.

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Background Gene prediction on DNA has been conducted using various deep learning architectures to discover splice sites to locate intron and exon regions. However, recent predictions are carried out with models trained with a sequence which has a splice site in the middle. This case eliminates the possibility of multiple splice sites in a single sequence. Results This research proposes a sequential labelling model to predict splice sites regardless of their position in a sequence. A sequential labelling model named DNABERT-SL is developed on pre-trained DNABERT-3. DNABERT-SL is benchmarked against the latest sequential labelling model for mutation type and location prediction based on BiLSTM and BiGRU. While achieving F1 scores above 0.8 on validation data, BiLSTM, BiGRU, and DNABERT-SL perform poorly on test data as indicated by their respective low F1 scores (0.498 ± 0.184, 0.6 ± 0.123, 0.532 ± 0.245). Conclusions DNABERT-SL model cannot distinguish nucleotides acting as splice sites from normal ones. Principal component analysis on token contextual representation produced by DNABERT-SL shows that the representation is not optimal for distinguishing splice site tokens from non-splice site tokens. Splice site motif observation conducted on test and training sequences shows that an arbitrary sequence with GT-AG motif can be both splice sites in some sequences and normal nucleotides in others.

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Abstract. Intermittent gas injection is a method to help oil production process. Gas is injected through choke in surface and then gas into tubing. Gas forms three areas in tubing: gas column area, film area and slug area. Gas column is used to propel slug area until surface. A mathematical model of intermittent gas injection is developed in gas column area, film area and slug area. Model is expanding based on mass and momentum conservation. Using assume film thickness constant in tubing, model has been developed by Tasmi et. al. [14]. Model consists of 10 ordinary differential equations. In this paper, assumption of pressure in gas column is uniform. Model consist of 9 ordinary differential equations. Connection of several variables can be obtained from this model. Therefore, dynamics of all variables that affect to intermittent gas lift process can be seen from four equations. To study the behavior of variables can be analyzed numerically and mathematically. In this paper, simple mathematically analysis approach is used to study behavior of the variables. Variables that affect to intermittent gas injection are pressure in upstream valve and in gas column. Pressure in upstream valve will decrease when gas mass in valve greater than gas mass in choke. Dynamic of the pressure in the gas column will decrease and increase depending on pressure in upstream valve.