Influence of freezing temperature on semen quality of rainbow trout (Oncorhynchus mykiss)

Trout production in Peru has export potential. However, there are no fry available to maintain production throughout the year and there is also a lack of egg production at certain times of the year. Therefore, the alternative is to cryopreserve semen for the non-reproductive season. The objective of the study was to determine the influence of freezing temperature on the quality of rainbow trout semen. The semen of 12 reproductive males was collected in the facilities of the Chucuito Research and Production Center of the National University of the Altiplano of Puno, which underwent a macroscopic, microscopic pre and post thaw seminal evaluation and the fertility rate was measured. Due to the effect of three freezing temperatures (-80 °C, -100 °C, -120 °C), cryopreservation had a decline curve of -20 °C / min. The fresh seminal parameters were similar to those reported by other researchers. While freezing had unfavorable effects on semen quality, the best results for activation time (51.33 sec) and vitality (35.33%) were obtained with -100 °C, but the higher motility was obtained with -120 °C (36.33%). Regarding fertility, the higher rate was obtained with -100 °C (70.97%), followed by -80 °C and -120 °C in which 68.86% and 64.34% were obtained, respectively. In conclusion, the results suggest that the tolerable freezing temperature of rainbow trout semen is around -100 °C, which is shown as a favorable alternative for the reproductive management of rainbow trout under natural hypobaric conditions of the Peruvian highlands

Approaches to forecast oil field performance at the well pads level and beta testing on oilfields of RN-Nyaganneftegaz (ROSNEFT)

Background. The study performed by Rosneft employees shown in this paper demonstrates approach and analytical methods that allows to forecast oil production at the level of minimal infrastructure units. These approaches are used to forecast long-term oil production and predict infrastructure blockage. The approach was partially automated by the authors. This made it possible to testing at giant Krasnoleninskoye oilfield. Aim. The study was performed in order to develop and test an approaches to forecast oil production of large oil fields with high detail levels. Materials and methods. Common methods of decline curve analysis and water-into-oil curve analysis were used in this work to analyze the precondition. The main feature of the approach is the analysis of precondition at the level of large well clusters and transfer it to the level of wells. Some of the actions were automated by new proprietary software and were tested at the giant brown field. The software was integrated with the corporate database. Results. An author’s approach has been developed. The approach allows to forecast oil production at the level of infrastructure units using analytical methods. Oil production of the giant brown field with high detail levels were forecasted using the proposed approaches and developed software. Conclusions. The results show that the developed approaches and software can be used to forecast mediumand long-term performance of producing oil fields in the conditions of existing external and infrastructural constraints.

Experimental Investigation of Hydrocarbon Gas Huff-N-Puff Injection into the Live-Oil Window of Eagle Ford

The hydrocarbon gas huff and puff (HnP) technique has been used to improve oil production in unconventional oil reservoirs where excess capacity of produced gas is available and hydrocarbon prices are in a range to result in an economically viable case. Eagle Ford (EF) is one of the largest unconventional oil plays in the United State where HnP has been applied for enhanced oil recovery (EOR) at reservoirs within various oil windows. Our previously published Huff-n-puff results on dead oil with produced gas from Eagle Ford (EF) showed the recovery factor of hydrocarbon varying from 40 to 58%. The objective of this paper is to extend the experiments to live oil with EF core plugs to investigate the mechanisms of HnP which are affected by the composition of injected gas and resident oil, injection and soaking time as well as injection/depletion pressure gradient. Eagle Ford live oil and natural gas produced from the target area were used for HnP tests. Four representative core plugs were used with the tests conducted at reservoir conditions (125 °C and 3,500 psi). The live oil experiments with four reservoir core plugs showed an improvement in oil recovery with recovery factor (RF) varying from 19.5 to 33 % in six cycles of HnP, whereas the primary depletion on the same core plug showed RF below 11 %. A lower recovery factor of HnP from live oil saturated core in this study was observed as compared to dead oil saturated core reported in a previous publication. It is attributed to a lesser diffusion effect on mass transfer between injected gas and resident oil when the core is saturated with live oil. This behavior is displayed by the pressure decline curve during the first soaking period. A sharper diffusion pressure decline occurred in the dead oil saturated core plug where a higher concentration gradient between injected gas and resident oil drives a faster gas transport into the oil due to the molecular diffusion during the soaking period.

Phototroph-heterotroph interactions during growth and long-term starvation across Prochlorococcus and Alteromonas diversity

Interactions among microorganisms are ubiquitous, yet to what extent strain-level diversity affects interactions is unclear. Phototroph-heterotroph interactions in marine environments have been studied intensively, due to their potential impact on ocean ecosystems and biogeochemistry. Here, we characterize the interactions between five strains each of two globally abundant marine bacteria, Prochlorococcus (a phototroph) and Alteromonas (a heterotroph), from the first encounter between individual strains and over more than a year of subsequent co-culturing. Prochlorococcus-Alteromonas interactions affected primarily the dynamics of culture decline, which we interpret as representing cell mortality and lysis. The shape of the decline curve and the carrying capacity of the co-cultures were determined by the phototroph and not the heterotroph strains involved. Comparing various models of culture mortality suggests that death rate increases over time in mono-cultures but decreases in co-cultures, with cells potentially becoming more resistant to stress. During 435 days of co-culture, mutations accumulated in one Prochlorococcus strain (MIT9313) in genes involved in nitrogen metabolism and the stringent response, indicating that these processes occur during long-term nitrogen starvation. Our results suggest potential mechanisms involved in long-term starvation survival in co-culture, and highlight the information-rich growth and death curves as a useful readout of the interaction phenotype.

Establishment and Verification On A Death Risk Model of Sepsis Patients Within 30 Days

Background/Objective: To establish and validate an individualized nomogram to predict the probability of death within 30 days in patients with sepsis would help clinical physicians to make correct decision. Methods We collected data of 1,205 patients with sepsis. These included 16 indexes like age and blood, randomly assigned to the modeling and verification groups. In the modeling group, the independent risk factors related to death within 30 days were analyzed. Besides, a nomogram was established to draw the receiver-operating characteristic (ROC) curve of the subjects. Subsequently, the discriminant ability of the model was evaluated by the area under the ROC curve (AUC). Then, a calibration chart and Hosmer-Lemeshow test were employed to evaluate the calibration degree of the model, and the Decline Curve Analysis (DCA) test was used to evaluate the clinical effect of the model. Results The different independent risk factors related to the death of sepsis patients within 30 days included pro-brain natriuretic peptide (pro.bnp), albumin, lactic acid (lac), oxygenation index, mean arterial pressure (map), and hematocrit (hct). The AUC of the modeling and verification groups were 0.815 and 0.806, respectively. Moreover, the P-values of the Hosmer-Lemeshow test in the two groups were 0.391 and 0.100, respectively, and the DCA curves of the two groups were both above the two extreme curves. Conclusion Our model presents good significance for predicting the death of sepsis patients within 30 days. Therefore, there is a need to implement this model in clinical practice, as prompt prediction could help tailor treatment regimens and enhance survival outcomes.

Conditions for Effective Application of the Decline Curve Analysis Method

Determining the reliable values of the filtration parameters of productive reservoirs is the most important task in monitoring the processes of reserve production. Hydrodynamic studies of wells by the pressure build-up method, as well as a modern method based on production curve analysis (Decline Curve Analysis (DCA)), are some of the effective methods for solving this problem. This paper is devoted to assessing the reliability of these two methods in determining the filtration parameters of terrigenous and carbonaceous productive deposits of oil fields in the Perm Krai. The materials of 150 conditioned and highly informative (obtained using high-precision depth instruments) studies of wells were used to solve this problem, including 100 studies conducted in terrigenous reservoirs (C1v) and 50 carried out in carbonate reservoirs (C2b). To solve the problem, an effective tool was used—multivariate regression analysis. This approach is new and has not been previously used to assess the reliability of determining the filtration parameters of reservoir systems by different research methods. With its use, a series of statistical models with varying degrees of detail was built. A series of multivariate mathematical models of well flow rates using the filtration parameters determined for each of the methods is constructed. The inclusion or non-inclusion of these filtration parameters in the resulting flow rate models allows us to give a reasonable assessment of the possibility of using the pressure build-up method and the DCA method. All the constructed models are characterized by high statistical estimates: in all cases, a high value of the determination coefficient was obtained, and the probability of an error in all cases was significantly less than 5%. As applied to the fields under consideration, it was found that both methods demonstrate stable results in terrigenous reservoirs. The permeability determined by the DCA method and the pressure build-up curve does not control the flow of the fluid in carbonate reservoirs, which proves the complexity of the filtration processes occurring in them. The DCA method is recommended for use to determine the permeability and skin factor in the conditions of terrigenous reservoirs.

An Integrated Workflow for Reserves Evaluation in the U.S. Permian Basin Based on SPEE Monograph 3

In 2011, the Society of Petroleum Evaluation Engineers (SPEE) published Monograph 3 as an industry guideline for reserves evaluation of unconventionals, especially for probabilistic approaches. This paper illustrates the workflow recommended by Monograph 3. The authors also point out some dilemmas one may encounter when applying the guidelines. Finally, the authors suggest remedies to mitigate limitations and improve the utility of the approach. This case study includes about 300 producing shale wells in the Permian Basin. Referring to Monograph 3, analogous wells were identified based on location, geology, drilling-and-completion (D&C) technology; Technically Recoverable Resources (TRRs) of these analogous wells were then evaluated by Decline Curve Analysis (DCA). Next, five type-wells were developed with different statistical characteristics. Lastly, a number of drilling opportunities were identified and, consequently, a Monte Carlo simulation was conducted to develop a statistical distribution for undeveloped locations in each type-well area. The authors demonstrated the use of probit plots and demonstrated the binning strategy, which could best represent the study area. The authors tuned the binning strategy based on multiple yardsticks, including median values of normalized TRRs per lateral length, slopes of the distribution lines in lognormal plots, ratios of P10 over P90, and well counts in each type-well category in addition to other variables. The binning trials were based on different geographic areas, producing reservoirs, and operators, and included the relatively new concept of a "learning curve" introduced by the Society of Petroleum Engineers (SPE) 2018 Petroleum Resources Management System (PRMS). To the best of the authors’ knowledge, this paper represents the first published case study to factor in the "learning curves" method. This paper automated the illustrated workflow through coded database queries or manipulation, which resulted in high efficiencies for multiple trials on binning strategy. The demonstrated case study illustrates valid decision-making processes based on data analytics. The case study further identifies methods to eliminate bias, and present independent objective reserves evaluations. Most of the challenges and situations herein are not fully addressed in Monograph 3 and are not documented in the regulations of the U.S. Security and Exchange Commission (SEC) or in the PRMS guidelines. While there may be differing approaches, and some analysts may prefer alternate methods, the authors believe that the items presented herein will benefit many who are starting to incorporate Monograph 3 in their work process. The authors hope that this paper will encourage additional discussion in our industry.

Is Decline Curve Analysis the Right Tool for Production Forecasting in Unconventional Reservoirs?

This study presents a novel metaheuristic algorithm that uses a physics-based model for multi-fractured horizontal wells (MFHW) to accurately predict the estimated ultimate recovery (EUR) for unconventional reservoirs. The metaheuristic algorithm creates a sizeable number of stochastic simulations and keeps the simulation results from those random models that closely reproduce observed production data. Unlike other optimization methods, the proposed algorithm does not aim at finding the exact solution to the problem but a group of sufficiently accurate solutions that help to construct the partial solution to the optimization problem as a function of production history. Results from this work provide sufficient evidence as to why traditional decline curve analysis (DCA) is not a suitable solution for production forecasting in unconventional reservoirs. Two case studies are discussed in this work where results from both modeling strategies are compared. Evolutionary prediction of EUR over time using DCA behaves erratically, regardless of the amount of historical production data available to the regression model. Such erratic behavior can, in turn, yield an erroneous estimation of key economic performance indicators of an asset. In contrast, the proposed metaheuristic algorithm delivers precise and accurate results consistently, achieving a significant reduction of uncertainties as more production data becomes available. In conclusion, the proposed partial optimization approach enables the accurate calculation of important metrics for unconventional reservoirs, including production forecasting and expected productive life of an asset.