A reservoir's static bottomhole pressure is an integral component of many reservoir evaluation disciplines. The static bottomhole pressure is normally acquired through gauge measurements; however, this method has disadvantages such as cost and mechanical risk. Accordingly, the ability to accurately estimate the static bottomhole pressure would provide a cost-effective and safe alternative to well intervention. In this work, a new cloud computing method is introduced to predict the static bottomhole pressure of a natural gas well. The method reaps the benefits of available IR 4.0 technologies, namely multi-layered high performance software computing. The utilization of an advanced software codes enabled accurate and timely prediction of gas wells’ bottomhole pressures. This method differs from existing methods by utilizing the apparent molecular weight profiling concept. Based on the inputs of pressure and temperature gradient data, an iterative calculation scheme is applied to produce a well-specific molecular weight profile. This profile is used along with a modified form of the equation of state to perform top node pressure calculations and ultimately predict the static bottomhole pressure for gas wells. The new calculation method was applied on two calculation modes: calibration mode and time lapse mode. In the calibration mode, the static bottomhole pressure is predicted on the same gradient survey used to generate the apparent molecular weight profile. On the other hand, the time lapse calculation mode predicts the static bottomhole pressure after a period of time has elapsed from the gradient survey used to build the molecular weight profile. The top node method was tested rigorously, and the prediction results were found accurate with low error percentages.
This paper will present an alternative calculation technique to predict wellbore crossflow rate in a water injection well resulting from a casing leak. The method provides a self-governing process for wellbore related calculations inspired by the fourth industrial revolution technologies. In an earlier work, calculations techniques were presented which do not require the conventional use of downhole flowmeter (spinner) to obtain the flow rate. Rather, continuous surface injection data prior to crossflow development and shut-in well are used to estimate the rate. In this alternative methodology, surface injection data post crossflow development are factored in to calculate the rate with the same accuracy. To illustrate the process an example water injector well is used. To quantify the casing leak crossflow rate, the following calculation methodology was applied:Generate a well performance model using pre-crossflow injection data. Normal modeling techniques are applied in this step to obtain an accurate model for the injection well as a baseline case.Generate an imaginary injection well model: An injection well mimicking the flow characteristics and properties of the water injector is envisioned to simulate crossflow at flowing (injecting) conditions. In this step, we simulate an injector that has total depth up to the crossflow location only and not the total depth of the example water well.Generate the performance model for the secondary formation using post crossflow data: The total injection rate measured at surface has two portions: one portion goes into the shallower secondary formation and another goes into the deeper (primary) formation. The modeling inputs from the first two steps will be used here to obtain the rate for the downhole formation at crossflow conditions.Generate an imaginary production well model: The normal model for the water injector will be inversed to obtain a production model instead. The inputs from previous steps will be incorporated in the inverse modeling.Obtaining the crossflow rate at shut-in conditions: Performance curves generated from step 3 & 4 will be plotted together to obtain an intersection that corresponds to the crossflow rate at shut-in conditions. This numerical methodology was analytically derived and the prediction results were verified on syntactic field data with very high accuracy. The application of this model will benefit oil operators by avoiding wireline logging costs and associated safety risks with mechanical intervention.
Gas deviation factor (z-factor) and other gas reservoir fluid properties, such as formation volume factor, density, and viscosity, are normally obtained from Pressure-Volume-Temperature (PVT) experimental analysis. This process of reservoir fluid characterization usually requires collecting pressurized fluid samples from the wellbore to conduct the experimental work. The scope of this paper will provide an alternative methodology for obtaining the z-factor. An IR 4.0 tool that heavily utilizes software coding was developed. The advanced tool uses the novel apparent molecular weight profiling concept to achieve the paper objective timely and accurately. The developed tool calculates gas properties based on downhole gradient pressure and temperature data as inputs. The methodology is applicable to dry, wet or condensate gas wells. The gas equation of state is modified to solve numerically for the z-factor using the gradient survey pressure and temperature data. The numerical solution is obtained by applying an iterative computation scheme as described below:A gas apparent molecular weight value is initialized and then gas mixture specific gravity and pseudo-critical properties are calculated.Gas mixture pseudo-reduced properties are calculated from the measured pressure and temperature values at the reservoir depth.A first z-factor value is determined as a function of the pseudo-reduced gas properties.Gas pressure gradient is obtained at the reservoir depth from the survey and used to back-calculate a second z-factor value by applying the modified gas equation of state.Relative error between the two z factor values is then calculated and compared against a low predefined tolerance.The above steps are reiterated at different assumed gas apparent molecular weight values until the predefined tolerance is achieved. This numerical approach is computerized to perform the highest possible number of iterations and then select the z-factor value corresponding to the minimum error among all iterations. The proposed workflow has been applied on literature data with known reservoir gas properties, from PVT analysis, and showed an excellent prediction performance compared to laboratory analysis with less than 5% error.
The digitally transformative Upstream Well Integrity Surveillance Excellence (U-WISE) software technology was built. U-WISE data driven processes provide a risk-based financial optimization model inspired by IR 4.0's big data analytics. The objective of U-WISE software technology is to continuously optimize financial resources related to the frequency of conducting well integrity surveys. The new technology balances the calculated well integrity risk with the associated financial impact for the entire integrity surveillance program. U-WISE software technology application constitute a paradigm shift in the well integrity surveillance portfolio of oilfield operators. The U-WISE software technology development was started by analyzing thousands of historical well integrity data. The big data analytics optimization schemes embedded in U-WISE software technology was initially developed based on a total of 38,104 case studies from different well and fluid types. U-WISE software technology runs artificial-intelligence based queries to collect health and defect data pertaining to integrity surveys. The data were conditioned for the analytics by recording health and defect time events. Then, the data were run through statistical schemes to obtain probability of health, defect, and overall probability of failure. The models’ product is a risk of failure percentage specific to a survey and well type, representative of all conditions. The risk of failure percentages are used to run surveillance optimization scenarios and quantify the financial impacts from such optimization. U-WISE software technology continues to perform the optimization on real-time data based on new field collected data. The overall combined optimization results from applying the U-WISE software technology are substantial annual savings. There are other tangible benefits to this optimization in availing more crude for production by reducing well shut-in time for integrity surveys. The revamped well integrity frequencies based on the IR 4.0 optimization furnished by U-WISE software technology serves as an industry benchmark for proficient and fiscally-responsible asset integrity monitoring. The reliability of wells integrity is now greatly improved with the updated procedures, technologies, and integrity standards set forth by the IR 4.0 based U-WISE software and resulting instruction manual. Wells’ production is now more efficient and sustained based on the optimized well surveillance shut-in times. Safety and integrity of the wells are now quantified and balanced via the new U-WISE software technology and kept at the required tolerable risk levels. Wells intact integrity strengthens environmental protection by reducing and eliminating undesirable well integrity events such as well downhole or surface leaks and the resulting aquifer and air contamination. Well integrity surveys were performed based on best oilfield practices. With the abundance of historical data, it became possible to prudently evaluate the well integrity risks and balance these risks with costs of conducting the surveys to achieve optimization.
This paper presents a workflow based on big data analytics to model the reliability of downhole Inflow Control Valves (ICVs) and predict their failures. The paper also offers economic analysis of optimum ICV stroking frequency to maintain valves functionality at the lowest possible cost to the oilfield operator. Installing an ICV in a petroleum well is a costly process and is done by a drilling or workover rig. As such, maintaining a fully functional ICV throughout the lifecycle of a well is important to ensure proper return on investment. ICVs are known to malfunction if not periodically stroked/cycled. The action of stroking ensures that each valve opening is free from obstructing material that would prevent the ICV from operating between one valve opening step to another. When an ICV malfunctions, a costly functionality restoration operation is sometime required without guaranteed results. In other cases, the valve is declared no longer useful and the asset cannot be further utilized due to malfunction. In this paper, an analytical decision making model to predict failures of ICVs is presented that is based on rigorous big data analytics. The model factors in the frequency of stroking before a valve fails. Then, an economic analysis accounting for the CAPEX & OPEX of an ICV is included to optimize the stroking frequency. The utilized techniques include ICV failure and stroking records and classifying the data into pre-defined criteria. Cumulative probability distribution functions are defined for each data set and used to generate failure probability functions. The probability equations are factored into an asset management cost scheme to minimize expected maintenance costs and probability of ICV failure. The results of applying this novel methodology to any smart well clearly showed maximized ICV service life and proper return of investment. The results demonstrate that ICVs lifecycle was prolonged with low maintenance cycling cost. Methodologies similar to the one presented in this paper are true manifestation of the fruitful impact IR4.0 technologies have on oilfields day-to-day operations.
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