Richard Gary Hughes

Production Data Analysis of Shale Gas Reservoirs

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A Large Scale Network Model To Obtain Interwell Formation Characteristics

Limited data availability and poor data quality make it difficult to characterise many reservoirs. For waterflooded reservoirs, production and injection data provide information from which injector-to-producer connections can be inferred. In this research, well locations and injection and production rate data are used to develop a reservoir-scale network model. A Voronoi mesh divides the reservoir into node volumes, each of which contains a well. Bonds connect the nodes with conductance values that are inferred from the rate data. The inverse problem minimises the mean-squared difference between computed and observed production data by adjusting the conductances between nodes. A derivative free optimisation algorithm is used to minimise the mean-squared difference. This coarse network model approach is fast and efficient because it solves for a small number of unknowns and is less underdetermined than correlation-based methods. The reservoir network model has promise as a reservoir description tool because of its modest data requirements, flexibility, efficiency, interpretability, and dynamism. [Received: July 17, 2015; Accepted: January 14, 2016]

Response surface method for assessing energy production from geopressured geothermal reservoirs

Developing low-enthalpy geothermal resources along the US Gulf Coast is attractive for reducing global warming and providing clean energy. In this work, synthetic yet representative models for typical geopressured geothermal reservoirs located along the US Gulf Coast are considered. A Box–Behnken experimental design is used to select a small set of these models to perform detailed reservoir simulation runs. Full quadratic linear models are fit to the simulation results, and their sufficiency is confirmed by comparing them to kriging response surfaces. To achieve a higher degree of efficiency in using the response surfaces, Hammersley sequence sampling (HSS) method is used instead of traditional Monte Carlo sampling. HSS ensures that the factor space is sampled more uniformly and the response distribution is converged in less time. By evaluating these proxy models in the sampled factor space, the sensitivity and uncertainty of the response to the factors can be assessed. In this work, the sensitivity and uncertainty of engineered convection is assessed. For quantifying engineered convection, five uncertain reservoir attributes were selected. The response was defined as the net extracted enthalpy. In particular, two different designs for harvesting energy from geothermal reservoirs were compared using the response surfaces. In the modeled systems, results show that the regular design is more effective than the reverse design for extracting energy from geopressured geothermal reservoirs.

Statistical modeling of geopressured geothermal reservoirs

Identifying attractive candidate reservoirs for producing geothermal energy requires predictive models. In this work, inspectional analysis and statistical modeling are used to create simple predictive models for a line drive design. Inspectional analysis on the partial differential equations governing this design yields a minimum number of fifteen dimensionless groups required to describe the physics of the system. These dimensionless groups are explained and confirmed using models with similar dimensionless groups but different dimensional parameters. This study models dimensionless production temperature and thermal recovery factor as the responses of a numerical model. These responses are obtained by a Box-Behnken experimental design. An uncertainty plot is used to segment the dimensionless time and develop a model for each segment. The important dimensionless numbers for each segment of the dimensionless time are identified using the Boosting method. These selected numbers are used in the regression models. The developed models are reduced to have a minimum number of predictors and interactions. The reduced final models are then presented and assessed using testing runs. Finally, applications of these models are offered. The presented workflow is generic and can be used to translate the output of a numerical simulator into simple predictive models in other research areas involving numerical simulation. HighlightsScreening models for identifying attractive geothermal reservoirs are presented.The scaling analysis of the model produced fifteen dimensionless numbers.Important dimensionless numbers are found using statistical algorithms.Uncertainty violin plots are used to introduce dimensionless time into the models.The presented workflow is useful for translating simulation results into models.

Risk Based Approach to Identify the Leakage Potential of Wells in Depleted Oil and Gas Fields for CO 2 Geological Sequestration

The selection of depleted oil and gas fields as potential CO2 geological storage sites has both positive and negative aspects that need to be considered. The positives are that the storage capacity or pore volume can be reliably estimated from field’s production history, and reservoir characterization can be performed with more readily available well, log or seismic data without additional expenses. The main drawback is the presence of wells in the field, as each well may provide a leakage pathway for injected CO2. The leakage potential of a well is a function of its proximity to injection wells, cement coverage in the potential storage zone, well abandonment conditions including cementing of the annular space, and the nature of any barriers to prevent CO2 leakage to the surface. Qualitative and quantitative risk-based approaches can be used to identify the wells that have comparatively higher leakage probabilities in comparison to other wells. The objective of this study is to use a risk-based approach to identify and categorize wells based on their leakage potential in depleted oil and gas fields. This will not only help in planning injection strategies but may also help in selection of remediation strategies. The model may be presented well by using the Fault Tree Analysis (FTA) technique. It implements screening criteria and a tier-based approach in which wells are screened and categorized into different tiers based on different well characteristics. The well characteristics include the physical distance from injection wells, the quality and portion of cement coverage of wells in the target zone, the regulations at the time of well completion, the leakage potential of sealing barriers for the targeted zone, the number of overlying shale and sand intervals and leakage of either CO2 or brine to shallower wells, the nature and quality of permanent or temporary well abandonment procedures, and the quality and length of annular space covered with cement for shallower well casings or sections. Existing models for well leakage are used to quantitatively estimate the leakage rate. The risk of leakage is presented qualitatively and quantitatively in the form of leaked CO2 volume to shallow aquifers or to the atmosphere. The approach is used for a representative depleted oil and gas field in southern Louisiana to show an example application of the process. The developed model provides a means to systemically identify the wells that are more likely to leak and have high consequences. Due to the reduced order nature of the tool, it should prove to be a useful tool in the planning and execution phase of the CO2 sequestration process.

Static and Dynamic CO 2 Storage Capacity Estimates of a Potential CO 2 Geological Sequestration Site in Louisiana Chemical Corridor

The close proximity of large CO2 emitters and depleted oil and gas reservoirs in the Louisiana Chemical Corridor (LCC) provide unique opportunities for CO2 geological sequestration in coastal Louisiana. The identification of sites with good storage capacity and retention characteristics is of prime importance for successful CO2 storage projects. In this study, the Bayou Sorrel field area located within close proximity of some of the large CO2 emitters in the LCC, is analyzed as a potential candidate site for aquifer storage. The results of static and dynamic aquifer storage capacity estimates are presented in this study. A volumetric approach is used to estimate the static storage capacity, and reservoir simulations are performed to compute dynamic storage capacity. The field and well data from publically available data sources are compiled to characterize the sands for prospective CO2 sequestration intervals (i.e., non-productive sands), and pressure and temperature conditions. Information of total areal extent, gross formation thickness, and total porosity are used along with a storage efficiency factor to find the pore volume available for storage. The upper depth limit for CO2 injection is dictated by the pressure and temperature conditions at which CO2 exists in a supercritical state. The Peng-Robinson (PR) equation of state is used in conjunction with subsurface pressure and temperature to determine the minimum depth at which CO2 is supercritical. Multiple geological realizations are used for a realistic site specific storage capacity estimate. The reservoir simulations capture the transient nature of the process and provide estimation of storage capacity under dynamic conditions. The sensitivity of injection location and boundaries is also evaluated in the dynamic storage capacity estimates. The results of the dynamic storage capacity estimate for a 1,000 ft thick interval at an average depth of 7,100 ft show that reasonable values of storage efficiency factors for this region are in the range of 1.14 to 2%. The results of the dynamic model also show that the nature of the storage zone boundary type, end point saturation and injection rate play significant role in estimation of dynamic storage capacity. These factors may induce more than 30% change in estimated dynamic storage value. The calculated storage efficiency factor may be applicable to other potential sites in this region, having similar geological characteristics.

A Study of Fluid Flow in Sediments and the Effect of Tidal Pumping

Offshore drilling and production operations can result in spills or leaks of hydrocarbons into seabed sediments, which can potentially contaminate these sediments with oil. If this oil later migrates to the water surface it has the potential for negative environmental impacts. For proper contingency planning and to avoid larger consequences in the environment, it is essential to understand mechanisms and rates for hydrocarbon migration from oil containing sediments to the water surface as well as how much will remain trapped in the sediments. It is believed that the amount of oil transported out of the sediment can be affected by tidal pumping, a common form of subterranean groundwater discharge (SGD). However, we could find no study experimentally investigating the phenomenon of fluid flow in subsea sediments containing oil and the effects of tidal pumping. This study presents an experimental investigation of tidal pumping to determine if it is a possible mechanism that may contribute to the appearance of an oil sheen on the ocean surface above a sediment bed containing oil. An experimental apparatus was constructed of clear PVC pipe allowing for oil migration to be monitored as it flowed out of a sand pack containing oil, while tidal pressure oscillations were applied in three different manners. The effect of tidal pumping was simulated via compression of air above the water (which simulated the increasing static head from tidal exchange). Experimental results show that sustained oil release occurred from all tests, and tests with oscillating pressure produced for longer periods of time. Furthermore, the experimental results showed that the oil migration rate was affected by grain size, oil saturation, and oscillation wave type. In all oscillating experiments the rate and ultimate recovery was less than the comparable static experiments. For the conditions studied, the experimental results indicate that with an oscillating pressure on top of a sand pack, movement of a non-replenishing source of oil is suppressed by pressure oscillation.