This paper will share production engineering perspectives on the deployment of the sensor ball technology. The sensor ball is a new integrated production logging platform that was developed to measure primarily wellbore downhole pressure and temperature. The reduction in size of the innovative tool sensors has enabled packing of the tool in an attractive wireless manner. The sensor ball records wellbore data and stores collected data on electronic memory chips. The scope of this paper is to explain the working mechanism of the technology and outline the proper methodology for deployment by recapping the operational events of a successful sensor ball field trials on two water wells. The sensor ball is deployed and collected from the wellhead tree cap in a carefully designed sequence of operational steps to ensure no damage to the tool or tree valves can take place. The sensor ball has dissolvable weight element that allows for the tool to descend to the target measurement depth. The tool is made from material, with a specific density, that allows for buoyancy floating in water after the release of dissolvable weight. Conventional wireline gradient pressure and temperature surveys preceded the sensor ball runs during the trial phase. The objective of the wireline surveys is to provide a basis that qualifies the sensor ball data and quantifies its accuracy with respect to gauge measurements. The developed field trial test success criteria were: Successful mechanical deployment/retrieval of the sensor ball within programmed timeComplete data recovery from the sensor ball upon retrievalAccuracy of recovered data compared to wireline surveys below 5% average absolute error The comparison results of the sensor ball and wireline data at both water wells showed consistency and accuracy. The average absolute error was less than 3 % for pressure and temperature data. Further, the field trial has met all developed success criteria. The deployment of the sensor ball technology by oilfield operators will enable realization of several tangible benefits such as reducing acquisition time of downhole data compared to wireline and reducing manpower requirements for pressure and temperature surveys. The sensor ball enhancement to manpower requirement can allow for simultaneous data acquisition operations that were not possible with wireline surveys. Oilfield operators can avoid direct costs related to wireline units and associated equipment and its maintenance. Further, the typical mechanical risks associated with well intervention can be eliminated by deploying the sensor ball technology.
This paper will introduce a pioneering Iron Sulfide (FeS) chemical dissolver. The novel chemical dissolver can be used for descaling applications in oil and gas wells. However, the particular scope of this paper would be to outline an operational well intervention workflow for a successful deployment of FeS dissolver in water wells. Iron sulfide scale presence in the wellbore is challenging to oilfield operators. Unlike other common scale types, such as Calcium Carbonates, FeS scale does not dissolve easily in Hydrochloric Acid (HCl) and the FeS/HCl reaction produces the dangerous Hydrogen Sulfide as a reaction product. Accordingly, oilfield operators resort to mechanical descaling of the FeS in order to restore wellbore accessibility. Mechanical descaling has disadvantages that include high cost, the need to flowback downhole milling returns, and possible damage to downhole tubulars. Alternatively, the novel chemical described in this paper can be used to effectively descale the wellbore without exposing the oilfield operator to the setbacks that could encountered from mechanical descaling. This paper will outline operational steps to execute a flagship descaling operation. The operational steps include pre-job diagnostics, job design and contingencies, as well as expected results and outcomes. The proposed FeS dissolving chemical has successfully met all laboratory and field criteria to descale iron sulfide scale and stimulated well injectivity. The deployment of the FeS dissolver can result in 67 % increase in overall well injection rate compared to pre-chemical descaling rate. Other tangible benefits to this chemical include avoidance of rig workover operations to pull and replace downhole tubing in cases where mechanical descaling was not successful in restoring wellbore access. The new chemical allows for conducting safer descaling operation by reducing risks associated with reproduction of hydrogen sulfide, formation damage from non-friendly chemicals, and downhole equipment damage by corrosion. The chemical also has the ability to dissolve carbonates for near wellbore cleanup. The dissolver has a 70 % enhanced dissolving ability comparing to 15% HCl.
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.
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