The main role of infill drilling is either adding incremental reserves to the already existing one by intersecting newly undrained (virgin) regions or accelerating the production from currently depleted areas. Accelerating reserves from increasing drainage in tight formations can be beneficial considering the time value of money and the cost of additional wells. However, the maximum benefit can be realized when infill wells produce mostly incremental recoveries (recoveries from virgin formations). Therefore, the prediction of incremental and accelerated recovery is crucial in field development planning as it helps in the optimization of infill wells with the assurance of long-term economic sustainability of the project. Several approaches are presented in literatures to determine incremental and acceleration recovery and areas for infill drilling. However, the majority of these methods require huge and expensive data; and very time-consuming simulation studies. In this study, two qualitative techniques are proposed for the estimation of incremental and accelerated recovery based upon readily available production data. In the first technique, acceleration and incremental recovery, and thus infill drilling, are predicted from the trend of the cumulative production (Gp) versus square root time function. This approach is more applicable for tight formations considering the long period of transient linear flow. The second technique is based on multi-well Blasingame type curves analysis. This technique appears to best be applied when the production of parent wells reaches the boundary dominated flow (BDF) region before the production start of the successive infill wells. These techniques are important in field development planning as the flow regimes in tight formations change gradually from transient flow (early times) to BDF (late times) as the production continues. Despite different approaches/methods, the field case studies demonstrate that the accurate framework for strategic well planning including prediction of optimum well location is very critical, especially for the realization of the commercial benefit (i.e., increasing and accelerating of reserve or assets) from infilled drilling campaign. Also, the proposed framework and findings of this study provide new insight into infilled drilling campaigns including the importance of better evaluation of infill drilling performance in tight formations, which eventually assist on informed decisions process regarding future development plans.
In this paper, we propose a method using continuous wavelets to study the multivariate fractional Brownian motion through the deviations of the transformed random process to find an efficient estimate of Hurst exponent using eigenvalue regression of the covariance matrix. The results of simulations experiments shown that the performance of the proposed estimator was efficient in bias but the variance get increase as signal change from short to long memory the MASE increase relatively. The estimation process was made by calculating the eigenvalues for the variance-covariance matrix of Meyer’s continuous wavelet details coefficients.
In this paper, we propose a method using continuous wavelets to study the multivariate fractional Brownian motion through the deviations of the transformed random process to find an efficient estimate of Hurst exponent using eigenvalue regression of the covariance matrix. The results of simulations experiments shown that the performance of the proposed estimator was efficient in bias but the variance get increase as signal change from short to long memory the MASE increase relatively. The estimation process was made by calculating the eigenvalues for the variance-covariance matrix of Meyer’s continuous wavelet details coefficients.
Spatial Autoregressive Model (SAR) is one of the modeling frameworks that indicates a spatial dependence in the response variable. SAR model has a weakness, which is represented by the unknown variance of the residuals. Therefore, an alternative model has used titled Spatial Autoregressive Quantile Regression (SARQR) model That which is obtained by combining SAR and Quantile Regression (QR) models, is a regression method with the approach of dividing the data into particular quantiles that are likely to have different estimate values. This alternative model addresses the variance issues in SAR models. Additionally, the SARQR model not only resolves the issue of spatial variance but also serves as a solution for dealing with non-normal data
... Show MoreBackground: The incisive canal is an anatomical structure with an important location in the anterior maxilla, analyzing this canal and its relation to the bone anterior to the canal is necessary during dental implant. Aim of this study is evaluated effect of gender, age and tooth loss in area of maxillary central incisors teeth on the dimensions of incisive canal and buccal bone anterior to the canal using spiral computed tomography. Materials and Methods: Sample consists of prospective study for 156 subjects for both gender, they divided into two groups, 120 dentate group (60 male and 60 female) with age ranging from (20-70) and 36 edentate group (with missing maxillary central incisors) (18 male and 18 female) with age ranging from (50-70
... Show MoreIn order to evaluate the effect of seed size, plant growth regulators and some chemical materials on seed vigour and seedling growth of rice (Oryza sativa L.) an experiment was conducted in 2015 at Laboratories of Agriculture and Marshes College, University of Thi-Qar. Factorial experiment in CRD was used with four replications in two factors. The first factor included three seed sizes (4.6-5.1, 5.2-5.7 and 5.8-6.3 mm). The second factor was seeds soaking treatments (KNO3 6 gl-1, CaCl2 20 gl-1, salicylic acid 20 mg l-1, cytokinin 40 mg l-1, gibberllic acid 400 mg l-1, ascorbic acid 40 mg l-1 and seeds soaked in distilled water). The results showed that the largest seed size influenced significantly and gave the higher averages of germinatio
... Show MoreThe research was carried out in lathhouse on one-year-old apple seedlings of the Ibrahimi variety in the Karma-Fallujah region for the 2021 growing season to study the effect of methods of adding nano-fertilizer and humic acid on seedling growth. A two-factor experiment was designed according to a randomized complete block design, with three replicates and two seedlings per experimental unit, so the number of seedlings was 54. The first factor includes NPK nanofertilizer at three levels (0- and 2-ml L-1 foliar spray and 5 ml L-1 soil application). The second factor is humic acid at three levels (0 and 5 g of seedlings - 1 foliar spray and 10 g of seedlings - 1 soil application). The results of the study show that the NPK nano-fertil
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