Guiding Principles of USGS Methodology for Assessment of Undiscovered Conventional Oil and Gas Resources

During the last 30 years, the methodology for assessment of undiscovered conventional oil and gas resources used by the Geological Survey has undergone considerable change. This evolution has been based on five major principles. First, the U.S. Geological Survey has responsibility for a wide range of U.S. and world assessments and requires a robust methodology suitable for immaturely explored as well as maturely explored areas. Second, the assessments should be based on as comprehensive a set of geological and exploration history data as possible. Third, the perils of methods that solely use statistical methods without geological analysis are recognized. Fourth, the methodology and course of the assessment should be documented as transparently as possible, within the limits imposed by the inevitable use of subjective judgement. Fifth, the multiple uses of the assessments require a continuing effort to provide the documentation in such ways as to increase utility to the many types of users. Undiscovered conventional oil and gas resources are those recoverable volumes in undiscovered, discrete, conventional structural or stratigraphic traps. The USGS 2000 methodology for these resources is based on a framework of assessing numbers and sizes of undiscovered oil and gas accumulations and the associated risks. The input is standardized on a form termed the Seventh Approximation Data Form for Conventional Assessment Units. Volumes of resource are then calculated using a Monte Carlo program named Emc2, but an alternative analytic (non-Monte Carlo) program named ASSESS also can be used. The resource assessment methodology continues to change. Accumulation-size distributions are being examined to determine how sensitive the results are to size-distribution assumptions. The resource assessment output is changing to provide better applicability for economic analysis. The separate methodology for assessing continuous (unconventional) resources also has been evolving. Further studies of the relationship between geologic models of conventional and continuous resources will likely impact the respective resource assessment methodologies.     

Probabilistic methodology for estimation of undiscovered petroleum resources in play analysis of the United States

A geostochastic system called FASPF was developed by the U.S. Geological Survey for their 1989 assessment of undiscovered petroleum resources in the United States. FASPF is a fast appraisal system for petroleum play analysis using a field-size geological model and an analytic probabilistic methodology. The geological model is a particular type of probability model whereby the volumes of oil and gas accumulations are modeled as statistical distributions in the form of probability histograms, and the risk structure is bilevel (play and accumulation) in terms of conditional probability. The probabilistic methodology is an analytic method derived from probability theory rather than Monte Carlo simulation. The resource estimates of crude oil and natural gas are calculated and expressed in terms of probability distributions. The probabilistic methodology developed by the author is explained.The analytic system resulted in a probabilistic methodology for play analysis, subplay analysis, economic analysis, and aggregation analysis. Subplay analysis included the estimation of petroleum resources on non-Federal offshore areas. Economic analysis involved the truncation of the field size with a minimum economic cutoff value. Aggregation analysis was needed to aggregate individual play and subplay estimates of oil and gas, respectively, at the provincial, regional, and national levels.     

Development of an Improved Methodology to Assess Potential Unconventional Gas Resources

Considering the important role played today by unconventional gas resources in North America and their enormous potential for the future around the world, it is vital to both policy makers and industry that the volumes of these resources and the impact of technology on these resources be assessed. To provide for optimal decision making regarding energy policy, research funding, and resource development, it is necessary to reliably quantify the uncertainty in these resource assessments. Since the 1970s, studies to assess potential unconventional gas resources have been conducted by various private and governmental agencies, the most rigorous of which was by the United States Geological Survey (USGS). The USGS employed a cell-based, probabilistic methodology which used analytical equations to calculate distributions of the resources assessed. USGS assessments have generally produced distributions for potential unconventional gas resources that, in our judgment, are unrealistically narrow for what are essentially undiscovered, untested resources. In this article, we present an improved methodology to assess potential unconventional gas resources. Our methodology is a stochastic approach that includes Monte Carlo simulation and correlation between input variables. Application of the improved methodology to the Uinta–Piceance province of Utah and Colorado with USGS data validates the means and standard deviations of resource distributions produced by the USGS methodology, but reveals that these distributions are not right skewed, as expected for a natural resource. Our investigation indicates that the unrealistic shape and width of the gas resource distributions are caused by the use of narrow triangular input parameter distributions. The stochastic methodology proposed here is more versatile and robust than the USGS analytic methodology. Adoption of the methodology, along with a careful examination and revision of input distributions, should allow a more realistic assessment of the uncertainty surrounding potential unconventional gas resources.     

Deep Natural Gas Resources

From a geological perspective, deep natural gas resources generally are defined as occurring in reservoirs below 15,000 feet, whereas ultradeep gas occurs below 25,000 feet. From an operational point of view, deep may be thought of in a relative sense based on the geologic and engineering knowledge of gas (and oil) resources in a particular area. Deep gas occurs in either conventionally trapped or unconventional (continuous-type) basin-center accumulations that are essentially large single fields having spatial dimensions often exceeding those of conventional fields.Exploration for deep conventional and continuous-type basin-center natural gas resources deserves special attention because these resources are widespread and occur in diverse geologic environments. In 1995, the U.S. Geological Survey estimated that 939 TCF of technically recoverable natural gas remained to be discovered or was part of reserve appreciation from known fields in the onshore areas and state waters of the United States. Of this USGS resource, nearly 114 trillion cubic feet (Tcf) of technically recoverable gas remains to be discovered from deep sedimentary basins. Worldwide estimates of deep gas also are high. The U.S. Geological Survey World Petroleum Assessment 2000 Project recently estimated a world undiscovered conventional gas resource outside the U.S. of 844 Tcf below 4.5 km (about 15,000 feet).Less is known about the origins of deep gas than about the origins of gas at shallower depths because fewer wells have been drilled into the deeper portions of many basins. Some of the many factors contributing to the origin and accumulation of deep gas include the initial concentration of organic matter, the thermal stability of methane, the role of minerals, water, and nonhydrocarbon gases in natural gas generation, porosity loss with increasing depth and thermal maturity, the kinetics of deep gas generation, thermal cracking of oil to gas, and source rock potential based on thermal maturity and kerogen type. Recent experimental simulations using laboratory pyrolysis methods have provided much information on the origins of deep gas.Technologic problems are among the greatest challenges to deep drilling. Problems associated with overcoming hostile drilling environments (e.g. high temperatures and pressures, and acid gases such as CO₂ and H₂S) for successful well completion, present the greatest obstacles to drilling, evaluating, and developing deep gas fields. Even though the overall success ratio for deep wells (producing below 15,000 feet) is about 25%, a lack of geological and geophysical information continues to be a major barrier to deep gas exploration.Results of recent finding-cost studies by depth interval for the onshore U.S. indicate that, on average, deep wells cost nearly 10 times more to drill than shallow wells, but well costs and gas recoveries differ widely among different gas plays in different basins.Based on an analysis of natural gas assessments, deep gas holds significant promise for future exploration and development. Both basin-center and conventional gas plays could contain significant deep undiscovered technically recoverable gas resources.     

Oil- and gas-resource assessment in certain South American Basins—An application of ARDS (Ver. 5.0) to complex exploration and discovery histories

The modified Arps-Roberts Discovery Process Modeling System [ARDS (Ver. 4.01)] has recently been upgraded [ARDS (Ver. 5.0)] and applied to a wide variety of field discovery and wildcat drilling data with differing characteristics. ARDS is designed to forecast the number and sizes of undiscovered fields in an exploration play or basin by using historical drilling and discovery data. Fields used as input may be grown or ungrown. Two models for field growth—one offshore and the other onshore—have been implemented (Schuenemeyer and Drew, 1996). Uncertainty attributable to field growth is estimated via simulation. This upgrade of ARDS has been designed to handle situations when the data cannot be partitioned into homogeneous regions, but where estimation of the number of remaining oil and gas fields is still meaningful. In this upgrade of ARDS, many restrictions, which include those on the number of fields and wildcat wells required to forecast the size distribution of the oil and gas fields that remain to be discovered in an exploration play, a basin, or other target area, have been removed. In addition, flexibility has been gained by reforming the criteria for convergence of the model. In all, 32 basins and subbasins in South America were examined, 18 of which had sufficient data to be amenable to forecasting the field-size distribution of undiscovered oil and gas resources directly by using the Petroconsultants Inc. (1993) field discovery and wildcat drilling data. Overall, ARDS (Ver. 5.0) performed well in estimating the field-size distribution of undiscovered oil and gas resources in the 18 basins and subbasins. The aggregate volume of undiscovered petroleum resources was characterized by using histograms of the distribution of resources and the following five statistics: the mean, the 80% trimmed mean, and the 10,50 (median), and 90 quantiles. More than 38 billion barrels of oil equivalent (BOE) in fields that contain more than one million BOE individually were forecast as remaining to be discovered. The largest basin, the Campos (Brazil), is forecast to contain nearly 10 billion BOE undiscovered resources. The East Venezuela Basin (excluding the Furrial Trend) is forecast to contain about 8 billion BOE; the Austral-Magallanes Basin (Argentina and Chile), about 7 billion BOE; and the Napo (Colombia and Ecuador) and the Neuquen (Argentina) Basins, between 3 billion and 4 billion BOE. A subset of these basins that illustrate the increased flexibility of ARDS are discussed.     

A probabilistic method for resource appraisal in a petroleum play and its application

A probabilistic method that is based upon conditional probability theory and the laws of expectation has been developed for estimating the undiscovered oil and gas resources in a petroleum play. It takes into account the favorable geological conditions that influence the accumulation of oil and gas and those factors which influence the distribution and the quantity of undiscovered oil and gas. Information about the number and size of undiscovered resources is provided. A practical application in the Turpan-Hami Basin in northwestern China is described. An erratum to this article is available at .     

Annotated bibliography of methodology for assessment of undiscovered oil and gas resources

An annotated bibliography of methodology of assessment of undiscovered oil and gas resources is presented as a useful reference for those engaged in resource assessment. The articles that are included deal only with quantitative assessment of undiscovered or inferred resources. the articles in this bibliography are classified largely according to the major assessment method that was applied in each situation. Major assessment methods include areal and volumetric yield methods, field size distributions, historical extrapolation, deposit modeling, organic geochemical mass balance methods, and direct expert assessment. Other categories include mathematical tools, reserve growth/confirmation, quantitative characterization of undiscovered resources, and general topics. For the purpose of future updates, we solicit contributions of articles that may have been missed in the preparation of this bibliography.     

U.S. department of the interior U.S. geological survey: Fractal lognormal percentage analysis of the U.S. geological survey’s 1995 national assessment of conventional oil and gas resources

The U.S. Geological Survey periodically makes appraisals of the oil and gas resources of the Nation. In its 1995 National Assessment the onshore areas and adjoining State waters of the Nation were assessed. As part of the 1995 National Assessment, 274 conventional oil plays and 239 conventional nonassociated-gas plays were assessed. The two datasets of estimates studied herein are the following: (1) the mean, undiscovered, technically recoverable oil resources estimated for each of the 274 conventional oil plays, and (2) the mean, undiscovered, technically recoverable gas resources estimated for each of the 239 conventional nonassociatedgas plays. It was found that the two populations of petroleum estimates are both distributed approximately as lognormal distributions. Fractal lognormal percentage theory is developed and applied to the two populations of petroleum estimates. In both cases the theoretical percentages of total resources using the lognormal distribution are extremely close to the empirical percentages from the oil and nonassociated-gas data. For example, 20% of the 274 oil plays account for 73.05% of the total oil resources of the plays if the lognormal distribution is used, or for 75.52% if the data is used; 20% of the 239 nonassociated-gas plays account for 76.32% of the total nonassociated-gas resources of the plays if the lognormal distribution is used, or for 78.87% if the data is used     

Quality of the Log-Geometric Distribution Extrapolation for Smaller Undiscovered Oil and Gas Pool Size

The U.S. Geological Survey procedure for the estimation of the general form of the parent distribution requires that the parameters of the log-geometric distribution be calculated and analyzed for the sensitivity of these parameters to different conditions. In this study, we derive the shape factor of a log-geometric distribution from the ratio of frequencies between adjacent bins. The shape factor has a log straight-line relationship with the ratio of frequencies. Additionally, the calculation equations of a ratio of the mean size to the lower size-class boundary are deduced. For a specific log-geometric distribution, we find that the ratio of the mean size to the lower size-class boundary is the same. We apply our analysis to simulations based on oil and gas pool distributions from four petroleum systems of Alberta, Canada and four generated distributions. Each petroleum system in Alberta has a different shape factor. Generally, the shape factors in the four petroleum systems stabilize with the increase of discovered pool numbers. For a log-geometric distribution, the shape factor becomes stable when discovered pool numbers exceed 50 and the shape factor is influenced by the exploration efficiency when the exploration efficiency is less than 1. The simulation results show that calculated shape factors increase with those of the parent distributions, and undiscovered oil and gas resources estimated through the log-geometric distribution extrapolation are smaller than the actual values.     

Recovery Factors of Oil Resources in China

This paper provides a new method to estimate recovery factors of oil resources. The China National Petroleum Assessment (2003–2007) (CNPA 2007) evaluates in-place oil resources and applies the recovery factor (RF) to estimate recoverable oil resources. The RF of oil resources plays an important role in the CNPA 2007. Based on the geological features, 24 types of oil assessment units are defined, such as the Mesozoic rift unit, the Mesozoic and Cenozoic foreland unit, etc. Through the recovery factor statistics of oil reserves (discovered) in different accumulations, as well as the potential analyses of enhanced petroleum recovery, appropriate RF valuing standards of oil resources (discovered and undiscovered) in different assessment units are developed. Calculation methods of oil resource RFs are established, including the appraisal standards, scoring, and calculation steps of oil resource RFs. Through the case studies, the valuing and appraisal standards of oil resource RFs are verified. Robust appraisal standards allow the RF method to be a valuable tool to effective assessment of China’s recoverable oil resources.     

Long-term implications of new U.S. gas estimates

The US Geological Survey’s 1995 estimates of domestic undiscovered plus undeveloped natural gas nearly tripled quantities estimated in its 1989 Assessment. Much of the increase came from selected unconventional resources assessed using the paradigm of continuous-type accumulations. These include such seemingly unrelated “unconventional” gas occurrences as “tight gas,” coalbed gas, gas in shales, and deep basin-center gas. Though only a small fraction of the assessed 352 trillion cubic feet is now economic, the quantity is nevertheless significant. Moreover, the lowest cost resources are close to major gas markets where competing conventional gas is modest. With continued technological improvements these resources can contribute significantly to future U.S. gas supply, even without subsidies     

Use of closure area and resources per unit area for assessing undiscovered petroleum resources in part of the Cooper Basin,South Australia

Undiscovered petroleum resources may be assessed using a computer program that simulates drilling each trap in a play and estimates the size of each potential discovery by multiplying together estimates of area of closure (A _cles) and resources per unit area (V _A ). There are several ways of determining values forA _cles andV _A so that the method can be widely applied. As an example, the sales gas potential of part of the Cooper Basin in South Australia is assessed using statistical projections of loglinear models to determine values forA _cles andV _A . A discontinuity in these particular data appears to result from an improvement in the quality and density of the seismic surveying, demonstrating that an assessment based on widely spaced or poor-quality seismic surveys can underestimate sales gas potential.     

Worldwide Estimates of Deep Natural Gas Resources Based on the U.S. Geological Survey World Petroleum Assessment 2000

The U.S. Geological Survey recently assessed undiscovered conventional gas and oil resources in eight regions of the world outside the U.S. The resources assessed were those estimated to have the potential to be added to reserves within the next thirty years. This study is a worldwide analysis of the estimated volumes and distribution of deep (>4.5 km or about 15,000 ft), undiscovered conventional natural gas resources based on this assessment. Two hundred forty-six assessment units in 128 priority geologic provinces, 96 countries, and two jointly held areas were assessed using a probabilistic Total Petroleum System approach. Priority geologic provinces were selected from a ranking of 937 provinces worldwide. The U.S. Geological Survey World Petroleum Assessment Team did not assess undiscovered petroleum resources in the U.S. For this report, mean estimated volumes of deep conventional undiscovered gas resources in the U.S. are taken from estimates of 101 deep plays (out of a total of 550 conventional plays in the U.S.) from the U.S. Geological Survey's 1995 National Assessment of Oil and Gas Resources. A probabilistic method was designed to subdivide gas resources into depth slices using a median-based triangular probability distribution as a model for drilling depth to estimate the percentages of estimated gas resources below various depths. For both the World Petroleum Assessment 2000 and the 1995 National Assessment of Oil and Gas Resources, minimum, median, and maximum depths were assigned to each assessment unit and play; these depths were used in our analysis. Two-hundred seventy-four deep assessment units and plays in 124 petroleum provinces were identified for the U.S. and the world. These assessment units and plays contain a mean undiscovered conventional gas resource of 844 trillion cubic ft (Tcf) occuring at depths below 4.5 km. The deep undiscovered conventional gas resource (844 Tcf) is about 17% of the total world gas resource (4,928 Tcf) based on the provinces assessed and includes a mean estimate of 259 Tcf of U.S. gas from the U.S. 1995 National Assessment. Of the eight regions, the Former Soviet Union (Region 1) contains the largest estimated volume of undiscovered deep gas with a mean resource of343 Tcf.     

Improved Minimum Miscibility Pressure Prediction for Gas Injection Process in Petroleum Reservoir

Determination of gas–oil minimum miscibility conditions is one of the important design parameters to improve the displacement efficiency of the hydrocarbon reservoir during enhanced oil recovery with gas injection. In this work, a support vector regression (SVR) model is developed using experimental data to estimate the minimum miscibility pressure (MMP) for various reservoir fluids and injection gases. Experimental MMP data taken from the reliable literature were used as input. Each data point input includes methane and intermediate components mole percent, plus fraction properties and reservoir temperature related to reservoir fluid and CO₂, H₂S, N₂ and intermediate mole fractions, and intermediate properties of the injected gas. Experimental MMP is regarded as the model output. The database contains 135 datasets, from which 125 datasets were used for model development, and the rest were used for model evaluation. Genetic algorithm was implemented to optimize the SVR model parameters. The proposed data-driven model was verified by statistical validation data. The model results illustrate a correlation coefficient (R²) of 0.999. In addition, the SVR results demonstrate the proposed model to be a fast tool and a robust approach to map input space to output features. The SVR model was compared to popular data-driven MMP estimation models as well. This comparison presents an acceptable accuracy relative to this estimation model. Finally, the presented model was evaluated against a comprehensive theoretical model of slim tube compositional simulation on a trusted literature dataset.     

A Framework for Expert Judgment to Assess Oil and Gas Resources

In frontier areas, where well data are sparse, many organizations have used expert judgment to estimate undiscovered resources. In this process, several important issues arise. How should the knowledge be elicited? At what level of aggregation (geologic process model, play, petroleum system, country, etc.) should the assessment be performed? How and at what stage of the assessment process should feedback be given to assessors? Is independent replication of estimates possible? How are issues of dependency treated? When and how should uncertainty be specified? The context for this presentation will be the methodology used in the US Geological Survey's 1998 1002-Arctic National Wildlife Refuge assessment of oil and gas resources.

     

Determination of grid size for digital terrain modelling in landscape investigations—exemplified by soil moisture distribution at a micro-scale

The central problem of a combined analysis of digital terrain models (DTMs) and other landscape data is determination of a DTM grid size (w) providing a correct study of relationships between topographic variables and landscape properties. Generally, an adequate w is determined by an expert estimate, and solutions are largely subjective. We developed an experimental statistical method to determine an adequate w for DTMs applied to landscape studies. The method includes the following steps: (a) derivation of a DTM set using a series of w_i , (b) performance of a correlation analysis of data on a landscape property and a topographic variable estimated with various w_i , (c) plotting of correlation coefficients obtained versus w, and (d) determination of smoothed plot portions indicating intervals of an adequate w. We applied the method developed to study the ifluence of topography on the spatial distribution of soil moisture (M) at a micro-scale. We investigated the dependence of M on gradient (G), horizontal (k_h ), vertical (k_v ), and mean (H) landsurface curvatures. For DTM derivation, we used 13 values of w_i from 1 to 7m. An interval of adequate w_i for M falls between 2.25 and 3.25m in the given terrain conditions. In absolute magnitudes, correlation coefficients are largest within this interval; correlation coefficients of M with G, k_h , k_v and H are 0.28, 0.52, 0.50 and 0.60, respectively, for w = 3m. The results obtained demonstrate that the method actually works to identify an adequate w at a micro-scale. The method developed allows estimation of an adequate area of landform which realise a topographic control of landscape properties.     

Assessment Methodology and Results From the Canadian Gas Potential Committee 2001 Report

Since 1991 volunteers from the Canadian Gas Potential Committee (CGPC) have conducted assessments of undiscovered gas potential in Canada. Reports were published in 1997 and 2001. The 2001 CGPC report assessed all established and some conceptual exploration plays in Canada and incorporated data from about 29,000 discovered gas pools and gas fields. Mainly year-end 1998 data were used in the analysis of 107 established exploration plays. The CGPC assessed gas in place without using economic cut offs. Estimates of nominal marketable gas were made, based on the ratio between gas in place and marketable gas in discovered pools. Only part of the estimated nominal marketable gas actually will be available, primarily because of restrictions on access to exploration and the small size of many accumulations. Most plays were assessed using the Petrimes program where it could be applied. Arps-Roberts assessments were made on plays where too many discovered pools were present to use the Petrimes program. Arps-Roberts assessments were corrected for economic truncation of the discovered pool sample. Several methods for making such corrections were tried and examples of the results are shown and compared with results from Petrimes. In addition to assessments of established plays, 12 conceptual plays, where no discoveries have been made, were assessed using Petrimes subjective methodology. An additional 65 conceptual plays were recognized, discussed, and ranked without making a quantitative assessment. No nominal marketable gas was attributed to conceptual plays because of the high risk of failure in such plays. Nonconventional gas in the form of coalbed methane, gas hydrates, tight gas, and shale gas are discussed, but no nominal marketable gas is attributed to those sources pending successful completion of pilot study projects designed to demonstrate commercially viable production. Conventional gas resources in Canada include 340 Tcf of gas in place in discovered pools and fields and 252 Tcf of undiscovered gas in place. Remaining nominal marketable gas includes 96 Tcf in discovered pools and fields and 138 Tcf of undiscovered nominal marketable gas. The Western Canada Sedimentary Basin holds 61% of the remaining nominal marketable gas. Future discoveries from that area will be mainly in pools smaller than 2.5 Bcf of marketable gas and increasing levels of exploratory drilling will be required to harvest this undiscovered resource. A pragmatic, geologically focussed approach to the assessment of undiscovered gas potential by the CGPC provides a sound basis for future exploration and development planning. Peer reviewed assessment on a play-by-play basis for entire basins provides both detailed play information and the ability to evaluate new exploration results and their impact on overall potential.     

1960-2007年中国地表潜在蒸散发敏感性的时空变化(英文)

Potential evapotranspiration (E0), as an estimate of the evaporative demand of the atmosphere, has been widely studied in the fields of irrigation management, crop water demand and predictions in ungauged basins (PUBs). Analysis of the sensitivity of E0 to meteorological factors is a basic research on the impact of climate change on water resources, and also is important to the optimal allocation of agricultural water resources. This paper dealt with sensitivity of E0 over China, which was divided into ten drainage systems, including Songhua River basin, Liaohe River basin, Haihe River basin, Yellow River basin, Yangtze River basin, Pearl River basin, Huaihe River drainage system, Southeast river drainage system, Northwest river drainage system and Southwest river drainage system. In addition, the calculation method of global radiation in Penman-Monteith formula was improved by optimization, and the sensitivities of Penman-Monteith potential evapotranspiration to the daily maximum temperature (STmax), daily minimum temperature (STmin), wind speed (SU2), global radiation (SRs) and vapor pressure (SVP) were calculated and analyzed based on the long-term meteorological data from 653 meteorological stations in China during the period 1960-2007. Results show that: (1) the correlation coefficient between E0 and pan evaporation increased from 0.61 to 0.75. E0 had the decline trends in eight of ten drainage systems in China, which indicates that "pan evaporation paradox" commonly exists in China from 1960 to 2007. (2) Spatially, Tmax was the most sensitive factor in Haihe River basin, Yellow River basin, Huaihe River drainage system, Yangtze River basin, Pearl River basin and Southeast river drainage system, and VP was the most sensitive factor in Songhua River Basin, Liaohe River basin, Northwest river drainage system while Rs was the most sensitive factor in Southwest river drainage system. For the nation-wide average, the most sensitive factor was VP, followed by Tmax, Rs, U2 and Tmin. In addition, the changes in sensitivity coefficients had a certain correlation with elevation. (3) Temporally, the maximum values of STmax and SRs occurred in July, while the maximum values of STmin, SVP and SU2 occurred in January. Moreover, trend analysis indicates that STmax had decline trends, while STmin, SU2, SRs and SVP had increasing trends.     

Geochemical Characteristics of Oil from Oligocene Lower Ganchaigou Formation Oil Sand in Northern Qaidam Basin,China

Oil from the Oligocene oil sands of the Lower Ganchaigou Formation in the Northern Qaidam Basin and the related asphaltenes was analyzed using bulk and organic geochemical methods to assess the organic matter source input, thermal maturity, paleo-environmental conditions, kerogen type, hydrocarbon quality, and the correlation between this oil and its potential source rock in the basin. The extracted oil samples are characterized by very high contents of saturated hydrocarbons (average 62.76%), low contents of aromatic hydrocarbons (average 16.11%), and moderate amounts of nitrogen–sulfur–oxygen or resin compounds (average 21.57%), suggesting that the fluid petroleum extracted from the Oligocene oil sands is of high quality. However, a variety of biomarker parameters obtained from the hydrocarbon fractions (saturated and aromatic) indicate that the extracted oil was generated from source rocks with a wide range of thermal maturity conditions, ranging from the early to peak oil window stages, which are generally consistent with the biomarker maturity parameters, vitrinite reflectance (approximately 0.6%), and T_max values of the Middle Jurassic carbonaceous mudstones and organic-rich mudstone source rocks of the Dameigou Formation, as reported in the literature. These findings suggest that the studied oil is derived from Dameigou Formation source rocks. Furthermore, the source- and environment-related biomarker parameters of the studied oil are characterized by relatively high pristane/phytane ratios, the presence of tricyclic terpanes, low abundances of C₂₇ regular steranes, low C₂₇/C₂₉ regular sterane ratios, and very low sterane/hopane ratios. These data suggest that the oil was generated from source rocks containing plankton/land plant matter that was mainly deposited in a lacustrine environment and preserved under sub-oxic to oxic conditions, and the data also indicate a potential relationship between the studied oil and the associated potential source rocks. The distribution of pristane, phytane, tricyclic terpanes, regular steranes and hopane shows an affinity with the studied Oligocene Lower Ganchaigou Formation oil to previously published Dameigou Formation source rocks. In support of this finding, the pyrolysis–gas chromatography results of the analyzed oil asphaltene indicate that the oil was primarily derived from type II organic matter, which is also consistent with the organic matter of the Middle Jurassic source rocks. Thus, the Middle Jurassic carbonaceous mudstones and organic rock mudstones of the Dameigou Formation could be significantly contributing source rocks to the Oligocene Lower Ganchaigou Formation oil sand and other oil reservoirs in the Northern Qaidam Basin.

     

Assessment of Exploration Bias in Data-Driven Predictive Models and the Estimation of Undiscovered Resources

The spatial distribution of discovered resources may not fully mimic the distribution of all such resources, discovered and undiscovered, because the process of discovery is biased by accessibility factors (e.g., outcrops, roads, and lakes) and by exploration criteria. In data-driven predictive models, the use of training sites (resource occurrences) biased by exploration criteria and accessibility does not necessarily translate to a biased predictive map. However, problems occur when evidence layers correlate with these same exploration factors. These biases then can produce a data-driven model that predicts known occurrences well, but poorly predicts undiscovered resources. Statistical assessment of correlation between evidence layers and map-based exploration factors is difficult because it is difficult to quantify the “degree of exploration.” However, if such a degree-of-exploration map can be produced, the benefits can be enormous. Not only does it become possible to assess this correlation, but it becomes possible to predict undiscovered, instead of discovered, resources. Using geothermal systems in Nevada, USA, as an example, a degree-of-exploration model is created, which then is resolved into purely explored and unexplored equivalents, each occurring within coextensive study areas. A weights-of-evidence (WofE) model is built first without regard to the degree of exploration, and then a revised WofE model is calculated for the “explored fraction” only. Differences in the weights between the two models provide a correlation measure between the evidence and the degree of exploration. The data used to build the geothermal evidence layers are perceived to be independent of degree of exploration. Nevertheless, the evidence layers correlate with exploration because exploration has preferred the same favorable areas identified by the evidence patterns. In this circumstance, however, the weights for the “explored” WofE model minimize this bias. Using these revised weights, posterior probability is extrapolated into unexplored areas to estimate undiscovered deposits.