It is widely recognized that predictions of hydrocarbon recovery are uncertain. In the area of reservoir engineering, the sources of uncertainty have three major causes:the model, because it is an imperfect representation of reality,geologic parameters, because of a limited sampling in space and/or time, andmeasurement errors in the experiments performed to determine inputs. Thus, a statistical treatment that recognizes both the lack of knowledge and the uncertainty of the parameters involved in the forecast of reservoir performance is desirable. Current stochastic-modeling approaches (Monte Carlo simulation and geostatistics) require extensive computational effort for realistic problems. There are numerous reasons for this, but the one that seems to present the largest hurdle is that reservoir predictions require a large number of input parameters. The thrust of this work is to find ways to perform uncertainty predictions with fewer parameters. This paper demonstrates several approaches that quantitatively estimate uncertainty in specific hydrocarbon-recovery predictions. These approaches include scaling, first-order and second-order analyses, response surface, and the Taguchi and Box-Behnken experimental designs. Numerical reservoir simulations were performed in two hypothetical reservoirs using each of the above methods to estimate uncertainty in oil-recovery efficiency. Results from these techniques are compared to experimental design and Monte Carlo simulations. The combination of scaling and the Box-Behnken experimental design can provide a reasonably accurate uncertainty estimation of hydrocarbon recovery with fewer simulation runs than the Monte Carlo simulation. In addition, we successfully reduced the turnaround time of the Monte Carlo simulation runs by using clusters in parallel of personal computers instead of one computer (e.g., more than 10 times faster with a cluster of 16 PCs). Experimental design and the use of multiple processors will reduce the computational effort in estimating uncertainty of a hydrocarbon-recovery prediction. This combination would also give rise to the possibility of exploring several existing methods in the design of experiments to lessen the burden of performing numerous large reservoir simulations. Introduction Reservoir uncertainty analysis seems to be important to achieving good reservoir management. Reservoir simulation studies play a significant role in the evaluation of different scenarios (i.e., geological or reservoir description) that affect the production forecast and the final oil recovery. These scenarios are the manifestation of the lack of information on or uncertainty about reservoir features. However, evaluating uncertainty involves effort and this is simply unjustifiable for many projects. Therefore, a practical procedure must reduce the effort in evaluating the uncertainty and yet retain a satis factory accuracy. The development of a method that can model and quantify uncertainty in reservoir simulation in an efficient and practical way is clearly needed. In this paper, we investigate a variety of approaches—scaling analysis, first-order and second-order analyses, experimental design, and response surface analysis—to estimate the uncertainty in a recovery prediction. These procedures are measured against experimental-design simulations and the traditional Monte Carlo procedure to compare their efficiency.

Fang oilfield is a small onshore reservoir in Thailand containing oil with gravity ranging from 20-40 °API and viscosity of approximately 10-120 cp. The depth of this field is from 300-1,200 meters and the sand thickness is varied from of 1 to 7 meters. For over 60 years of natural production, the field has low oil recovery. The difficulty has been attributed to the unfavorable production. Waterflooding, a secondary recovery, has been studied and the results show that the recovery can increase 4-6%. However, the production can be enhanced more with the tertiary recovery methods or enhanced oil recovery (EOR) which are recommended to increase oil production in this challenging field. However, the proper technologies have not been studied for commercial production yet. These technologies can be thermal recovery, gas injection, microbial enhanced oil recovery and chemical methods. Therefore, it will be the objective of this work to review and screen the EOR method to fit well with oil production in Fang oilfield in order to increase oil production. From this EOR screening, the main results from this study reveal that chemical injection is more appropriate for this field than other techniques because of reservoir characteristics and reservoir fluids. Based on the types of oil and composition in the oil, adding alkaline solution and surfactant can improve oil recovery because they can reduce interfacial tension of oil and to make the mobility higher. On the other hand, for other techniques, thermal recovery can increase mobility because of wax formation but it has higher cost. CO2 injection can increase more oil production. Nevertheless, the sources of CO2 has less supply and is expensive. Now, microbial enhanced oil recovery is on the beginning state and can provide the good potential. Consequently, based on the revision and screening method, the chemical method provide the higher commercial potential and feasibility to enhance oil production in Fang oilfield and the result of this study can be applied to design the plan of oil production operation in the oilfield in the future.

Drilling rigs -- Drill string -- Drill bits -- Drilling fluids -- Casing --Cementing -- Directional drilling -- Well control

The dynamic material balance methodology can be used to estimate gas initially-in-place using only production and PVT data. With this methodology, reservoir pressure is obtained without requiring the well to be shut in; it is therefore superior to the static material balance method since there is no loss of production. However, the technique requires iterative calculations and numerical integration of gas pseudotime and is quite complex to implement in practice. A simpler and equally accurate methodology is proposed in this study. It requires only production and PVT data and also does not rely on a shut-in pressure survey. In addition, it requires neither iterative calculations nor numerical integration of gas pseudotime. The results of the analysis include gas initially-in-place and gas productivity index, and can easily be extended to production forecasting. Gas initially-in-place is evaluated with a high degree of reliability. The methodology is successfully tested with two simulated cases and one field case, giving high-accuracy results.

Abstract-Gas condensate reservoirs have been challenging many researchers in petroleum industry for decades because of their complexities in flow behavior. After dew point pressure is reached, gas condensate will drop liquid out and increase liquid saturation near wellbore vicinity called condensate banking or condensate blockage. Hydraulic fracturing in horizontal direction has been proved to be a reliable method to mitigate condensate blockage and increase productivity of gas condensate well by means of pressure redistribution in the near wellbore vicinity. In this paper the parameters of dimensionless fracture conductivity and Stimulated Reservoir Volume (SRV) designs of lean and rich condensate compositions are studied. Well productivity and saturation profile of each design had been observed. The results from this study indicate that the higher dimensionless fracture conductivity gives the higher well productivity in every studied parameter in lean condensate composition. However, in rich condensate composition shows different trend of results because it has higher heavy ends (C 7+ ) that drop into liquid easier once pressure falls below dew point pressure. The maximum number of fracture and fracture permeability can be recognized in the study of rich condensate. In the study of SRV indicates that number of fracture is superior to fracture width in both gas and condensate productivity. Moreover, performing hydraulic fracturing can decrease pressure drawdown, production time and liquid dropout which leads to the mitigation of condensate banking near wellbore that can be recognized in the study of condensate saturation profile.