Dual Component Estimation; a probabilistic approach to estimate undiscovered oil and gas in an underexplored basin, Taranaki Basin, New Zealand

<p>New Zealand has relied on the supply of oil and gas from the Maui Field in the Taranaki Basin for 40 years. As this field nears depletion, there is considerable governmental encouragement for increased investment in exploration to ensure continued oil and gas production for domestic use and the export market. The Taranaki Basin covers a 100,000 km² area and has a 10 km-thick Cretaceous to Holocene sediment fill, which hosts only 200 exploration wells with 33 oil and gas discoveries ≥ 1 million barrels of oil equivalent (mmboe). Discoveries have been made throughout Paleocene to Holocene sequences in multiple reservoirs within onshore and offshore parts of the basin. A review of the hydrocarbon potential of the Taranaki Basin suggests that more than one working petroleum system operates in the basin, based on the distribution of oil and gas-condensate. As part of this study the most productive and prospective reservoir intervals have been studied to ascertain the working petroleum systems. The reservoir intervals are grouped into four plays and are referred to as the Cretaceous, Paleocene, Eocene and Miocene plays. The Dual Component Estimation (DCE) is a novel way of combining a modified, existing, size distribution-based discovery-sequence sampling method with a Geographical Information Systems (GIS)-based spatial method to estimate the amount and likely location of undiscovered oil and gas in an underexplored basin. In particular, the DCE uses an inverse sampling method to ensure the total number of all accumulations in the basin is not constrained by the size distributions of discoveries, which typically represent a very small proportion of accumulations in an underexplored basin. Furthermore, it is a probabilistic approach that captures ranges in uncertainties that result from using regional scale data and assumptions used to simplify the process of generating and trapping hydrocarbons. Given the underexplored character of the Taranaki Basin, this study has included potential discoveries to define the size distribution of the original population of all accumulations in a basin, which is used to derive the undiscovered volume. Potential discoveries are based on basin modelling and mapped structural traps. This approach increases the dataset of accumulations (discovered and modelled) from 33 to 338 and generates an original parent population that includes petroleum systems information from explored and unexplored areas of the basin. DCE modelling results suggest that the basin has an undiscovered oil and gas resource potential of ~1500 oil and gas accumulations totalling 8210–10800 mmboe. The mean discovery size is 328.7 mmboe; however the next discovery could be as large as 550–900 mmboe (with a 10% probability). More likely, the next discovery is estimated to be at least 50 mmboe (with a 90% probability) which is a commercially significant size in the Taranaki Basin. New discoveries in the Palaeocene play have been modelled in the Manaia anticline area, Western Platform and onshore eastern margin of the basin. It is most likely that a discovery in this play will be at least 40 mmboe, with a 90% probability; however it may be closer to 470 mmboe, with a 10% probability. The Eocene play is the most prospective and future discoveries will most likely be located along the eastern margin of the basin, nearshore and onshore of the western peninsula, and offshore, east of the Maui Field. There is a 10% probability that a new discovery in this play may be as big as 200–500 mmboe. More likely, with a 90% probability, a new discovery may be at least 35 mmboe. Discoveries in the Miocene play are most likely in the onshore peninsular area and in the offshore Northern Graben. There is a 90% probability that the next discovery may be at least 55 mmboe and a 10% probability that it may be much larger (310–800 mmboe). A discovery is yet to be made in the Cretaceous play. This study indicates that a new discovery in this play is most likely to be at least 50 mmboe (with a 90% probability) but may be greater and be at least 500 mmboe (with 10% probability). The strength of the DCE is in the use of additional geological data to include unexplored areas of the underexplored Taranaki Basin in the estimation. This estimation should also be applicable to other geologically similar, underexplored, sedimentary basins of New Zealand.</p>

Dual Component Estimation; a probabilistic approach to estimate undiscovered oil and gas in an underexplored basin, Taranaki Basin, New Zealand

<p>New Zealand has relied on the supply of oil and gas from the Maui Field in the Taranaki Basin for 40 years. As this field nears depletion, there is considerable governmental encouragement for increased investment in exploration to ensure continued oil and gas production for domestic use and the export market. The Taranaki Basin covers a 100,000 km² area and has a 10 km-thick Cretaceous to Holocene sediment fill, which hosts only 200 exploration wells with 33 oil and gas discoveries ≥ 1 million barrels of oil equivalent (mmboe). Discoveries have been made throughout Paleocene to Holocene sequences in multiple reservoirs within onshore and offshore parts of the basin. A review of the hydrocarbon potential of the Taranaki Basin suggests that more than one working petroleum system operates in the basin, based on the distribution of oil and gas-condensate. As part of this study the most productive and prospective reservoir intervals have been studied to ascertain the working petroleum systems. The reservoir intervals are grouped into four plays and are referred to as the Cretaceous, Paleocene, Eocene and Miocene plays. The Dual Component Estimation (DCE) is a novel way of combining a modified, existing, size distribution-based discovery-sequence sampling method with a Geographical Information Systems (GIS)-based spatial method to estimate the amount and likely location of undiscovered oil and gas in an underexplored basin. In particular, the DCE uses an inverse sampling method to ensure the total number of all accumulations in the basin is not constrained by the size distributions of discoveries, which typically represent a very small proportion of accumulations in an underexplored basin. Furthermore, it is a probabilistic approach that captures ranges in uncertainties that result from using regional scale data and assumptions used to simplify the process of generating and trapping hydrocarbons. Given the underexplored character of the Taranaki Basin, this study has included potential discoveries to define the size distribution of the original population of all accumulations in a basin, which is used to derive the undiscovered volume. Potential discoveries are based on basin modelling and mapped structural traps. This approach increases the dataset of accumulations (discovered and modelled) from 33 to 338 and generates an original parent population that includes petroleum systems information from explored and unexplored areas of the basin. DCE modelling results suggest that the basin has an undiscovered oil and gas resource potential of ~1500 oil and gas accumulations totalling 8210–10800 mmboe. The mean discovery size is 328.7 mmboe; however the next discovery could be as large as 550–900 mmboe (with a 10% probability). More likely, the next discovery is estimated to be at least 50 mmboe (with a 90% probability) which is a commercially significant size in the Taranaki Basin. New discoveries in the Palaeocene play have been modelled in the Manaia anticline area, Western Platform and onshore eastern margin of the basin. It is most likely that a discovery in this play will be at least 40 mmboe, with a 90% probability; however it may be closer to 470 mmboe, with a 10% probability. The Eocene play is the most prospective and future discoveries will most likely be located along the eastern margin of the basin, nearshore and onshore of the western peninsula, and offshore, east of the Maui Field. There is a 10% probability that a new discovery in this play may be as big as 200–500 mmboe. More likely, with a 90% probability, a new discovery may be at least 35 mmboe. Discoveries in the Miocene play are most likely in the onshore peninsular area and in the offshore Northern Graben. There is a 90% probability that the next discovery may be at least 55 mmboe and a 10% probability that it may be much larger (310–800 mmboe). A discovery is yet to be made in the Cretaceous play. This study indicates that a new discovery in this play is most likely to be at least 50 mmboe (with a 90% probability) but may be greater and be at least 500 mmboe (with 10% probability). The strength of the DCE is in the use of additional geological data to include unexplored areas of the underexplored Taranaki Basin in the estimation. This estimation should also be applicable to other geologically similar, underexplored, sedimentary basins of New Zealand.</p>

Bias in variance component estimation in swine crossbreeding schemes using selective genotyping and phenotyping strategies

Selective genotyping of crossbred (CB) animals to include in traditionally purebred (PB) dominated genetic evaluations has been shown to provide an increase in the response to selection for CB performance. However, the inclusion of phenotypes from selectively genotyped CB animals, without the phenotypes of their non-genotyped cohorts, could cause bias in estimated variance components (VC) and subsequent estimated breeding values (EBV). The objective of the study was to determine the impact of selective CB genotyping on VC estimates and subsequent bias in EBV when non-genotyped CB animals are not included in genetic evaluations. A swine crossbreeding scheme producing 3-way CB animals was simulated to create selectively genotyped datasets. The breeding scheme consisted of three PB breeds each with 25 males and 450 females, F1 crosses with 1200 females and 12,000 CB progeny. Eighteen chromosomes each with 100 QTL and 4k SNP markers were simulated. Both PB and CB performance were considered to be moderately heritable (h2=0.4). Factors evaluated were, 1) CB phenotype and genotype inclusion of 15% (n=1800) or 35% (n=4200), 2) genetic correlation between PB and CB performance (rpc=0.1, 0.5 or 0.7) and 3) selective genotyping strategy. Genotyping strategies included: a) Random: random CB selection, b) Top: highest CB phenotype and c) Extreme: half highest and half lowest CB phenotypes. Top and Extreme selective genotyping strategies were considered by selecting animals in full-sib (FS) families or among the CB population (T). In each generation, 4320 PB selection candidates contributed phenotypic and genotypic records. Each scenario was replicated 15 times. VC were estimated for PB and CB performance utilizing bivariate models using pedigree relationships with dams of CB animals considered to be unknown. Estimated values of VC for PB performance were not statistically different from true values. Top selective genotyping strategies produced deflated estimates of phenotypic VC for CB performance compared to true values. When using estimated VC, Top_T and Extreme_T produced the most biased EBV, yet EBV of PB selection candidates for CB performance were most accurate when using Extreme_T. Results suggest that randomly selecting CB animals to genotype or selectively genotyping Top or Extreme CB animals within full-sib families can lead to accurate estimates of additive genetic VC for CB performance and unbiased EBV.

An experimental combination of IGS repro3 campaign’s orbit products using a variance component estimation strategy

Over the past years, the International GNSS Service (IGS) has put efforts into reprocessing campaigns reanalyzing the full data collected by the IGS network since 1994. The goal is to provide a consistent set of orbits, station coordinates, and earth rotation parameters using state-of-the-art models. Different from the previous campaigns - namely: repro1 and repro2 - the repro3 includes not only GPS and GLONASS but also the Galileo constellation. The main repro3 objective is the contribution to the next realization of the International Terrestrial Reference Frame (ITRF2020). To achieve this goal, several Analysis Centers (AC) submitted their specific products, which are combined to provide the final solutions for each product type. In this contribution, we focus on the combination of the orbit products.We will present a consistent orbit solution based on a newly developed combination strategy where the weights are determined by a Least-Squares Variance Component Estimation (LSVCE). The orbits are combined in an iterative processing, first aligning all the products via a Helmert transformation, second defining which satellites will be used in the LSVCE, and finally normalizing the inverse of the variances as weights that are used to compute a weighted mean. Moreover, we will discuss the weight factors and their stability in the time evolution for each AC depending on the constellations. In addition, an external validation using a Satellite Laser Ranging (SLR) procedure will be shown for the combined solution.

An experimental combination of IGS repro3 campaign’s orbit products using a variance component estimation strategy

Over the past years, the International GNSS Service (IGS) has put efforts into reprocessing campaigns reanalyzing the full data collected by the IGS network since 1994. The goal is to provide a consistent set of orbits, station coordinates, and earth rotation parameters using state-of-the-art models. Different from the previous campaigns - namely: repro1 and repro2 - the repro3 includes not only GPS and GLONASS but also the Galileo constellation. The main repro3 objective is the contribution to the next realization of the International Terrestrial Reference Frame (ITRF2020). To achieve this goal, several Analysis Centers (AC) submitted their specific products, which are combined to provide the final solutions for each product type. In this contribution, we focus on the combination of the orbit products.We will present a consistent orbit solution based on a newly developed combination strategy where the weights are determined by a Least-Squares Variance Component Estimation (LSVCE). The orbits are combined in an iterative processing, first aligning all the products via a Helmert transformation, second defining which satellites will be used in the LSVCE, and finally normalizing the inverse of the variances as weights that are used to compute a weighted mean. Moreover, we will discuss the weight factors and their stability in the time evolution for each AC depending on the constellations. In addition, an external validation using a Satellite Laser Ranging (SLR) procedure will be shown for the combined solution.

A variance component estimation approach to infer associations between Mendelian polledness and quantitative production and female fertility traits in German Simmental cattle

Background Managing beneficial Mendelian characteristics in dairy cattle breeding programs implies that the correlated genetic effects are considered to avoid possible adverse effects in selection processes. The Mendelian trait polledness in cattle is traditionally associated with the belief that the polled locus has unfavorable effects on breeding goal traits. This may be due to the inferior breeding values of former polled bulls and cows in cattle breeds, such as German Simmental, or to pleiotropic or linkage effects of the polled locus. Methods We focused on a variance component estimation approach that uses a marker-based numerator relationship matrix reflecting gametic relationships at the polled locus to test for direct pleiotropic or linked quantitative trait loci (QTL) effects of the polled locus on relevant traits. We applied the approach to performance, health, and female fertility traits in German Simmental cattle. Results Our results showed no evidence for any pleiotropic QTL effects of the polled locus on test-day production traits milk yield and fat percentage, on the mastitis indicator ‘somatic cell score’, and on several female fertility traits, i.e. 56 days non return rate, days open and days to first service. We detected a significant and unfavorable QTL effect accounting for 6.6% of the genetic variance for protein percentage only. Conclusions Pleiotropy does not explain the lower breeding values and phenotypic inferiority of polled German Simmental sires and cows relative to the horned population in the breed. Thus, intensified selection in the polled population will contribute to increased selection response in breeding goal traits and genetic merit and will narrow the deficit in breeding values for production traits.

GPS-BDS-Galileo double-differenced stochastic model refinement based on least-squares variance component estimation

Stochastic models are essential for precise navigation and positioning of the global navigation satellite system (GNSS). A stochastic model can influence the resolution of ambiguity, which is a key step in GNSS positioning. Most of the existing multi-GNSS stochastic models are based on the GPS empirical model, while differences in the precision of observations among different systems are not considered. In this paper, three refined stochastic models, namely the variance components between systems (RSM1), the variances of different types of observations (RSM2) and the variances of observations for each satellite (RSM3) are proposed based on the least-squares variance component estimation (LS-VCE). Zero-baseline and short-baseline GNSS experimental data were used to verify the proposed three refined stochastic models. The results show that, compared with the traditional elevation-dependent model (EDM), though the proposed models do not significantly improve the ambiguity resolution success rate, the positioning precision of the three proposed models has been improved. RSM3, which is more realistic for the data itself, performs the best, and the precision at elevation mask angles 20°, 30°, 40°, 50° can be improved by 4⋅6%, 7⋅6%, 13⋅2%, 73⋅0% for L1-B1-E1 and 1⋅1%, 4⋅8%, 16⋅3%, 64⋅5% for L2-B2-E5a, respectively.