Using Raw Data From Oil Fields

The well tested in this project is located in the city of Brighton in Weld County, CO. Well SHABLE AB11-04P which is operated by Halliburton is one of the many wells in the Wattenberg field. Wattenberg field is a low permeability (“tight”) basin center gas field (Highley 12).Based from the Colorado Oil and Gas Conservation Commission in 1999, the Wattenberg field has approximately produced 1.75 TCFG, 76.4 MMBO, and 15.7 MMBW from all of the formation above. The primary source of hydrocarbon production in the Wattenberg field comes from the Muddy (“J”) Sandstone formation which currently has 1,900 producing wells. The Wattenberg formation also has a potential biogenic gas reserves for coalbed methane (CBM) production at the Laramie formation

America has been in an oil crisis for many years, it should stop. People and companies are using more oil than they should. Oil supplies are fragile. If the United States drills for oil in several other countries it would cost a lot of money and gas prices will increase. There is an option of drilling in Alaska for oil. If the United States did drill it would be cheaper because it is domestic. If the United States collected oil from Alaska's wildlife it would have an overall positive outcome.

For the purpose of this report, the commodity selected is natural gas will be discussed but focus will be on shale gas. The time period for analysing the factors affecting demand and supply of the shale gas will be from the 1990’s to date.

Reservoir Simulation is a significant exercise for decision making and field development planning. An important step of reservoir modeling is to address the uncertainty in geological properties of reservoir rocks. Geological properties such as porosity, permeability and saturations are obtained using different kinds of well logs measured during drilling and exploration phase. Due to the noisy and sparse nature of well logs, core samples, and seismic data, uncertainty becomes an intrinsic characteristic of any geological model. History matching is generally used to solve this problem and to estimate the spatially varying reservoir properties. History matching is a process of adjusting model parameters to obtain a model output similar to historical/dynamic

The coal samples used in this study are Yallourn (YL) and Morwell (MW) coal, two

The volume of water in the fresh water reservoirs, particularly those that are available for human use, are important water resources.[10

The deformation of the rock mass can be quantified by using the following equations evaluated by Deere and Miller (1966) as indicated in the table below.

Big Data in Oil and Gas industry is not something new. The industry has long dealt with huge amounts of data to make critical decisions over the period of time. For many years energy companies had invested in seismic software, data visualization and other digital tools & technologies for planning and optimization purposes. But now a day, most of the enterprises have started craving a certain desire for better execution of E&P activities. Since the crude oil prices have gone down significantly over the past few years, which have effectively brought down the profit margins. Also as world looks towards renewable resources of energy, companies are supposed to act in more efficient ways they ever had. This calls in for a digital transformation within the companies enabled by virtual integration through IT. This will of course require a lot of data gathering and analytics over it to drive the organizations towards success which will eventually justify investments and efforts made for it. As we know how big E&P operations are and at the scale they are carried out, there will be huge data points involved and data gathered will eventually lead into the realm of “Big Data”. According to a report from Bain & Company production can be improved by up to 6-8 % with the implementation of Big Data Analytics. But first of all let’s define Big Data acutely.

To begin building the model I had to input all of the necessary reservoir parameters for the field. This was accomplished via the geologic and field reports that I currently have access to. Within the CMG program I went to the array properties and created information for the initial model size like the sample below (Figure 1.1.) I also had to define the exact lithology I was operating in via the compressibility tab (Figure 1.2) Our Reservoir is also semi supported by a weak aquifer at the base of the formation so this was input into the model as well (Figure 1.3) The next step that I conducted was in creating a fluid model that would be utilized. Based upon the current observations and fluid tests from the field the model can be called a black oil model and thus that form of fluid was utilized. There is currently no exact fluid compatibility tests that have been performed in the region so we will operate with the base model without any operations now for the initial simulation (Figure 1.4) Next the rock fluid compatibility of the model was created from the above mentioned fluid compatibility to get relative permeability curves and other fluid components in order to create an even more accurate model (Figure 1.5-1.6) Next the conditions of our reservoir needed to be input into the model to represent our current conditions and the past production from the field. As the reservoir is relatively depleted already at this point we will lower the reservoir pressure and also adjust

In general sensitivity analysis, the most influencing parameters are identified through building a statistical linear model, partial t-test, and Analysis of Variance (ANOVA). Sensitivity analysis was conducted through Design of experiments (DoE) in order to determine the most influencing geological parameters on the Gas Assisted Gravity Drainage process performance. DoE combines multi-level of each parameter to create many computer experiments evaluated by the compositional reservoir simulation to obtain the flow response factor. In this study, the parameters adopted for sensitivity analysis are horizontal permeability, anisotropy ratio $K_{v}/K_{h}$, and porosity, all given for the entire reservoir. First, more than 80 computer

Archie Unleashed is an attempt to put the basic log analysis methodology for computing water saturation into a readable reference document. The beginning log analyst or petrophysicist should have little difficulty with the terms and concepts utilized in this paper, however, most terms are redefined in appendix A.

The formula is commonly used to study about the type and properties of fluid flow. Based on to the previous experiments conducted, RE is

Major Operations that this industry is involved in include the exploration and drilling of crude oil, and the extraction of natural gasses. Once the oil and gas is extracted, these companies now are responsible for processing the material to then be sold to industries that then either refine crude oil or distribute natural gas.

EnKF is a Monte Carlo data assimilation method, which has gained popularity over recent years for assisted history matching due to its ability to include available observations sequentially in time. Aanonsen et al. (2009) reviewed the application of EnKF in reservoir engineering for estimation of reservoir parameters. EnKF procedure utilizes an ensemble of model states (e.g. realizations of reservoir properties such as porosity and permeability) to estimate the covariance matrices used in model updating process. Initial ensemble is generated based on the prior knowledge of the reservoir derived from various sources as well logs, core and seismic analysis. In general, simulation techniques such as Sequential Gaussian Simulation (SGS) and Sequential Indicator Simulation (SIS) are used to generate multiple realizations. These realizations are consistent with the initial state of the reservoir. In the next step, all the reservoir models in ensemble are forwarded typically using the numerical reservoir simulation. Mean and covariance of predicted model states is calculated and used in turn to calculate Kalman gain. Next, in the update (or analysis) step, each geological

These issues are the optimization of settings (weight then again stream rate) in existing wells, optimization of the areas of new wells, and information digestion or history matching. The execution of the subsidiary free calculations is demonstrated to be truly adequate, particularly when they are actualized inside a disseminated nature 's domain.