Fracture diagnostic techniques are divided into several groups.[1] Direct far-field methods consists of tiltmeter-fracture-mapping and microseismic-fracture-mapping techniques. These techniques require sophisticated instrumentation embedded in boreholes surrounding the well to be fracture treated. When a hydraulic fracture is created, the expansion of the fracture causes the earth around the fracture to deform. Tiltmeters can be used to measure the deformation and to compute the approximate direction and size of the created fracture.

The single well chemical tracer (SWCT) test can be used to evaluate an Improved oil recovery (IOR) process quickly and inexpensively. The one-spot procedure takes advantage of the nondestructive nature of the SWCT method. The single-well (one-spot) pilot is carried out in three steps. First, Sor for the target interval is measured (see Residual oil evaluation using single well chemical tracer test. Then an appropriate volume of the IOR fluid is injected into the test interval and pushed away from the well with water.

The recent proliferation of subsurface data from instrumented wells has created significant challenges for traditional production-data-analysis methods to extract useful information for reservoir management. The approach has the potential to be used as a big-data analytic tool for long-duration production-data analysis to serve as a screening tool for selection of restimulation candidates. Restimulation treatments in producing shale wells have the potential to improve economic performance by increasing the conductivity of existing fractures or enhancing their contact with the formation. The influence of matrix and fracture characteristics on the success of restimulation, however, is not completely understood, which has led to uncertainty in determining favorable candidate wells. Several methods to select restimulation candidates have been proposed.

Kirkuk is a supergiant oil reservoir located in Iraq. Kirkuk began production in 1934, and 2 billion bbl of oil were produced before water injection was implemented in 1961. From 1961 to 1971, 3.2 billion bbl of oil were produced under pressure maintenance by waterdrive using river water. The 1971 production rate was approximately 1.1 million barrels of oil per day (BOPD). Since then, the field has continued to produce large volumes of oil by voidage-replacement water injection; however, few production details for recent years appear in the technical literature.

Casing drilling is a method by which the well is drilled and cased simultaneously. The small annulus from casing drilling can create a controllable dynamic equivalent circulating density (ECD). Casing-drilling technology enables obtaining the same ECD as with conventional drilling but with a lower (optimized) flow rate and lower rheological properties and mud weight. Frictional pressure loss during casing drilling was evaluated with computational fluid dynamics (CFD). Having accurate models for ECD, including the effects of pipe rotation and eccentricity in the annulus, is essential for success in these challenging jobs.

The oil and gas industry is facing unprecedented and brutal market conditions. While the industry was already in the midst of digitalization, the oil price crash has instilled a fresh impetus on its adoption to cut costs through innovation and new technologies. One such technology is predictive maintenance. When equipment on a rig breaks down, the resulting problem often is not that of replacement but the forced downtime in production or drilling. Therefore, predicting when equipment or a system is going to fail and determining the root cause of failure unlocks significant value.

Recently, many heavy-oil fields have seen exponentially higher volumes of data made available as a result of omnipresent connectivity. Existing data platforms have focused traditionally on solving the problem of data storage and access. The more-complex problem of true knowledge discovery and systematic value creation from the massive amount of data is less frequently addressed. The authors of this paper propose a novel work flow for the problem of building intelligent data analytics in heavy-oil fields. Optimal reservoir management for heavy-oil reservoirs requires systematic solutions that combine both engineering ability and advanced analytics.

A virtual metering system is an alternative way to measure the flow rate of a well in real time. They can therefore improve the reliability and the effectiveness of the production back allocation process, providing redundancy to multi-phase flow meters (MPFMs) measures.

Scope of this work is to build and validate two different virtual metering systems highlighting their peculiarities and comparing their performance.

A virtual metering system can estimate the flow rates of each well of an asset by elaboration of pressure and temperature data coming from the field.

In both virtual metering systems, the core is a fluid-mechanical model of the production network of the same off-shore field, made of six wells flowing in two parallel lines. The model is run multiple times adjusting the wells flow rates according to two different minimization algorithms, to match the measured data as much as possible.

After validating the results, the performance of the tools has been compared against MPFMs, used as a common reference.

Both systems have been validated against officially allocated flow rates coming from MPFMs. At first, only pressure data have been used as inputs.

System number one, which exploits the Matlab minimization algorithm and an OLGA model of the network, showed great accuracy in the majority of the cases, with an error less than 5%, making it a great verification tool for measures coming from other instruments. Its simulation runtime, however, is still too long to make it usable in a real-time application scenario.

System number two relies instead on the gradient descent algorithm and on a GAP model of the network. This system is equipped with an automatic tool that can discard unreliable signals coming from damaged or out-of-calibration field sensors, while simulating. The resulting accuracy is acceptable, with an average error between 5% and 8% and the computational time is short enough to be used as a live measurement tool, in parallel to MPFMs.

The validation process has been repeated adding temperatures to pressures in the input dataset. No accuracy improvement has been shown by the new results for both systems, concluding that a leaner structure where only pressure is considered is to be preferred.

The two systems represent economic measurement tools for production allocation, showing a good ability in supporting field operation in case of instrument failure or unreliability.

In the oil and gas exploration, well logs, the most convenient and economic data source, usually contain missing values due to various reasons. It is crucial to generate accurate synthetic logs for such missing intervals in terms of precise well log interpretation. In this paper, we propose a workflow of generating synthetic logs using cutting-edge machine learning techniques. Unlike existing methods, we exploit a generative model, which can deal with various missing patterns with a single model, and we combine it with a supervised model. With well log data of various regions, we show that our models accurately generate missing logs and outperforms existing supervised-only models. It is expected that our model is beneficial in the real field because of its performance and simplicity.

Abstract Gas breakthrough for horizontal oil producers can sometimes be an advantage for keeping the well producing, particularly in late life when the reservoir pressure is low and the water-cut is high. However, this additional gas may be unavailable after a long shut-in due to the gas front migrating upwards from the near wellbore. This results in a much lower inflow of gas during well start-up, which can make it difficult to overcome the hydrostatic head to kick off the flow. This paper is intended to summarize the study of such a case where a high GOR horizontal well failed to start up after a long shut-in. The study considered all given potential causes of the start-up failure such as mechanical restrictions and the presence of a hydrate plug in addition to the assumption that only the low GOR oil rim would contribute to the inflow during the early phase of the start-up. A commercially available advanced transient multiphase flow simulator was used to simulate each scenario and the results were then compared with the field measurements. It was determined that the missing reservoir gas influx at the start-up was the most likely reason for the failure of the well to restart. This conclusion has provided valuable input to the well intervention decision making. After the study was completed, a light well intervention was performed on the well and it was confirmed that there was no mechanical blockage or restriction in the wellbore.