Thermal maturity is an important parameter for commercial gas production from gas shale reservoirs if the shale has considerable organic content. There is a common idea that gas shale formations with higher potential for gas production are at higher thermal maturity status. Therefore estimating this parameter is very important for gas shale evaluation. The present study proposes an index for determining thermal maturity of the gas shale layers using the conventional well log data. To approach this objective, different conventional well logs were studied and neutron porosity, density and volumetric photoelectric adsorption were selected as the most proper inputs for defining a log derived maturity index (LMI). LMI considers the effects of thermal maturity on the mentioned well logs and applies these effects for modelling thermal maturity changes. The proposed methodology has been applied to estimate thermal maturity for Kockatea Shale and Carynginia Formation of the Northern Perth Basin, Western Australia. A total number of ninety eight geochemical data points from seven wells were used for calibrating with well log data. Although there are some limitations for LMI but generally it can give a good in-situ estimation of thermal maturity.
Thermal maturity and total organic carbon (TOC) are very important geochemical factors for evaluation of the gas shale reservoirs. There is a common hypothesis that gas shale layers with the higher potential for gas production (i.e. sweet spots) are located at the higher thermal maturity. Thermal maturity is an indicator for determining maximum temperature that a formation reached during different stages of hydrocarbon generation.
Liu, Zhen (Jiangsu University of Science and Technology) | Zhu, Renqing (Jiangsu University of Science and Technology) | Ji, Chunyan (Jiangsu University of Science and Technology) | Chen, Minglu (Jiangsu University of Science and Technology) | Teng, Bin (Dalian University of Technology) | Li, Liangbi (Jiangsu Modern Shipbuilding Technology Co. Ltd, Jiangsu University of Science and Technology)
Li, Zhigang (Offshore Oil Engineering Co. Ltd.) | He, Ning (Offshore Oil Engineering Co. Ltd.) | Duan, Menglan (Offshore Oil/Gas Research Center, China University of Petroleum) | Wang, Yingying (Offshore Oil/Gas Research Center, China University of Petroleum) | Dong, Yanhui (Offshore Oil/Gas Research Center, China University of Petroleum)
The prediction of dynamic elastic constants of reservoir rocks is one of the most important aspects of petroleum engineering. In recent years, several studies have been performed for this purpose. Because of uncertainty and variability in natural materials, deterministic prediction of rock properties in the reservoir is not reasonable. The purpose of this study is to evaluate uncertainty in dynamic-elastic-constant prediction for reservoir rock. Dipole-shear-sonic-image (DSI) log data from one of the Saudi Arabian reservoirs are used to evaluate uncertainty in dynamic-elastic-property prediction. For this purpose, a multiple linear regression (MLR) is carried out to present an empirical equation for shear-wave (S-wave) velocity prediction. Then, probabilistic analysis using Monte Carlo simulation (MCS) is performed to evaluate the uncertainty and reliability in prediction of dynamic elastic constants (Young's modulus and Poisson's ratio). On the basis of the analysis, uncertainty and variability of rock elastic constants are considered, and the value of Young's modulus and Poisson's ratio in a special interval from the reservoir are determined with a certain probability. Finally, the impact of log-data parameters on the value of rock elastic constants in the reservoir interval is assessed.
Shale gas exploration activities have been growing rapidly in Australia. A flow rate of up to 2 MMSCFD has been reported recently from the first exploratory vertical well in the Cooper Basin in South Australia. Perth and Canning Basins in Western Australia are also reported to be highly prospective. However, shale gas production differs from conventional reservoirs primarily because of extremely low permeability and other petrophysical characteristics. Commercial production requires massive hydraulic fracturing often in long horizontal completions.
The potential development of a shale gas field in Western Australia has been simulated to optimize production and minimize development cost through sensitivity analyses. Conditions in Australia are particularly challenging often because of significantly higher costs in drilling, completion and fracturing than those of the US. The minimum number of wells and the maximum Net Present Value (NPV) was iterated by simulation. The factors influencing their overall success of the field
development project were investigated in order to generate a workflow model suitable for a variety of cases. The influence of well fracture and other parameters such as completion length, fracture geometry, permeability and gas price was tested against NPV to optimize the development. Optimization of any development should be possible by iterating on any parameter and the related variables. Whilst in conventional gas there is a clear understanding of what is economically viable, this is not the case in shale gas particularly in Australia. Before embarking on any drilling, testing or development activities simulation sensitivity studies of this nature are essential.
Probabilistic methods for reserves estimation, including uncertainty quantification and probabilistic aggregation, have gained widespread acceptance in the oil and gas industry, since the first comprehensive guidelines were issued by the Society of Petroleum Engineers (SPE) in 2001. The probabilistic methods now used in the oil industry, as proposed in these guidelines, are similar to those also used in portfolio theory and risk management by the finance industry. A significant amount can be learned from the extensive experience with probabilistic methods and quantification of risk with measures [e.g., value-at-risk (VAR)] in financial risk management. Especially, the guidelines issued by the Basel II Accord (Bank for International Settlements 2006) and the discussions since the 2008 financial crisis contain important lessons.
In this paper, we examine a fundamental question: "Is the P90 reserves value an appropriate measure for quantifying the reserves' downside?" For the P90 reserves value to be considered a good measure of the reserves' downside, it needs to possess a number of basic characteristics involving P90 reserves for each field and the probabilistically aggregated P90 reserves for the portfolio of fields. Analogous to the definition of a coherent risk measure used in the finance industry, we define these characteristics for P90 reserves.
The P90 reserves are as good a risk measure as VAR used in the financial industry. However, like VAR, it is not a coherent risk measure. A possible uncertainty scenario, in which one of these necessary characteristics does not hold, is given. An alternative measure of risk for quantifying the reserves' downside, defined as the average reserves over the confidence interval higher than P90, is presented. This is a coherent risk measure.
In this paper, we highlight the appropriateness and limitations of using the P90 reserves estimate as a measure of the reserves' downside. Understanding of the limitations posed by using the P90 reserves value is vital in management of reserves risk.