Data Science Use Case for Brownfield Optimization - A Case Study

Data Science is the current gold rush. While many industries have benefitted from applications of data science, including machine learning and Artificial Intelligence (AI), the applications in upstream oil and gas are still somewhat limited. Some examples of applications of AI include seismic interpretations, facility optimization, and data driven modeling – forecasting. While still naïve, we will explore cases where data science can be used in the day to day field optimization and development. The Midway Sunset (MWSS) field in San Joaquin Valley, California has over 100 years of history. The field was discovered in 19011 and had limited development through the 1960s. Since the start of thermal stimulation in 1964, the field has seen phased thermal flooding and cyclic stimulation. Recently there has been an increase in heat mining vertical and horizontal wells to tap the remaining hot oil. As with any brownfield, the sweet spots are long gone. Effort is now to optimize the field development and tap by-passed oil, thereby increasing recovery. The current operational focus includes field wide holistic review of remaining resource potential. Resources in the MWSS reservoirs are produced by cyclic steam method. Cyclic thermal stimulation has been effective as an overall depletion process and for stimulating the near wellbore region to increase production. It is imperative to properly identify target wells and sands for cyclic stimulation. Cyclic steaming in depleted zones or cold reservoirs is often uneconomical. The benefit comes when we can identify and stimulate only the warm oil. Identification of warm oil and short listing the wells for cyclic stimulation is a labor-intensive process. The volume of data can get so large that it may not be feasible for a professional to effectively do the analysis. In this paper, we present a case study of data analytics for high grading wells for cyclic stimulation. This method utilizes the machine power to integrate reservoir, and production data to identify and rank wells for cyclic stimulation and potentially increase success rate by minimizing suboptimal cyclic candidates.

The Effect of Regular and Long Cyclic Steam Stimulation Method on Oil Production Performance of RUA Field in Central Sumatera

RUA field is classified into heavy oil reservoir type due to the high viscosity value and low API degree . This causes the RUA field can not be produced conventionally. the solution of this problem is to apply steam or thermal injection into reservoir which could reduce the viscosity of the heavy oil (Bera Babadagli, 2015). One of the best EOR methods that has been proven to overcome this issue is using CSS method (Suranto et al., 2020). During the production period, the CSS process can affect the viscosity of the oil by increasing the temperature of the oil in the reservoir. In one production well, cyclic work are applied periodically, its called repeated cyclic (J. J. Sheng, 2013). This is because time of reservoir temperature stays above the baseline temperature reservoir shortly. Even though the cyclic already done repeatedly, there is still a decrease of oil production, different peak reservoir temperatures, and found the possibility of pump damage after the cycle job which led to the need for analysis on these issues. The analysis was performed by looking at the historical production data, historical reservoir temperature data, and production pump work data in the RUA field. After a production history data that reprsentative analyzed, it was found that teh production after cyclic there is increasing, and there is also a decline from the previous cyclic production. Based on the results of the production analysis, it was found that 53.24% of the production wells in the RUA field were already in the ramp down stage and 46.75% were already in the ramp-up stage. Meanwhile, the average HET for regular cyclic jobs is 3-4 months and 5-6 months for long cyclic jobs. And from the pump work data, only 3 wells were damaged. This suggests that cyclic stimulation is completely safe to be performed in this field.

Modulation of the contractility of micropatterned myocardial cells with nanoscale forces using atomic force microscopy

The ability to modulate cardiomyocyte contractility is important for bioengineering applications ranging from heart disease treatments to biorobotics. In this study, we examined the changes in contraction frequency of neonatal rat cardiomyocytes upon single-cell-level nanoscale mechanical stimulation using atomic force microscopy. To measure the response of same density of cells, they were micropatterned into micropatches of fixed geometry. To examine the effect of the substrate stiffness on the behavior of cells, they were cultured on a stiffer and a softer surface, glass and poly (dimethylsiloxane), respectively. Upon periodic cyclic stimulation of 300 nN at 5 Hz, a significant reduction in the rate of synchronous contraction of the cell patches on poly(dimethylsiloxane) substrates was observed with respect to their spontaneous beat rate, while the cell patches on glass substrates maintained or increased their contraction rate after the stimulation. On the other hand, single cells mostly maintained their contraction rate and could only withstand a lower magnitude of forces compared to micropatterned cell patches. This study reveals that the contraction behavior of cardiomyocytes can be modulated mechanically through cyclic nanomechanical stimulation, and the degree and mode of this modulation depend on the cell connectivity and substrate mechanical properties.