A production choke is essential in regulating the pressure and flow rate of the produced hydrocarbon stream from a well in a field production system. Regulating the pressure and flow rate is necessary to achieve the production objectives of a field, which are to meet customer demand and optimally manage the reservoir over its life cycle. It is therefore important to have a means of effectively monitoring production choke performance to identify the onset of choke wear. Choke wear results from erosion of the choke due to impingement of particles carried in the produced hydrocarbon stream as it passes through the choke body. Choke wear increases the production rate and downstream pressure of a producing well, thereby upsetting the balance of the production system and resulting in ineffective field pressure management. Taking immediate action to replace a worn choke is therefore necessary to restore the production balance and achieve optimal pressure conservation of the production system. Choke performance monitoring is more critical for gas producing wells than oil producing wells because the velocity of gas is typically much higher than the velocity of oil, and consequently the risk and frequency of choke wear is much higher. Physical inspection of the production chokes to confirm wear for offshore gas wells is more laborious and time consuming due to the need to interrupt production, depressurize the flowline, decouple the choke body from the flowline, and ship the choke body onshore to the manufacturer's workshop for component inspections to confirm choke wear and, if necessary, choke replacement. A way to remotely monitor production choke performance and correctly detect choke wear for offshore gas wells without interrupting production is therefore operationally expedient, reduces exposure of personnel to unsafe rough sea conditions, and saves the cost of unnecessary physical choke inspections. The authors present a graphical method of monitoring the production rate and flowing tubing head pressure trends, with the choke size, of offshore high rate dry gas wells to detect choke wear using real-time production data. Examples of successful application of the method, which demonstrates that innovation can be simplification of processes, to detect choke wear and perform timely choke replacement are highlighted.

This study was carried out to separate acute myeloid leukemia (AML) blast cells and studies their proliferation in short-term culture. The separation procedure include three steps; Ficoll gradient separation, depletion of macrophages and depletion of (lymphocytes and of monocytes) for preparation of highly pure native AML blast cells from blood samples collected from patients with moderate blast percentage. Results showed that this procedure is an inefficient due to a decrease in total cell number and contamination with other cells after each separation step. Proliferation of native AML blast cells in short term-culture by cultivating isolated AML blast cells in RPMI medium supplemented with 20% human plasma at a concentration 1×106/ml in the presence and absence of colony -stimulating factor which was provided by conditioned media (PHA-leucocytes-, plasmacytoma cell line- and Hep-2 conditioned medium. The effect of each conditioned medium on proliferation of AML blast cells was studied separately. Results showed that plasmacytoma cell line conditioned medium didnot stimulate the proliferation of native AML blast cells, while cells seeded on media containing 10%PHA-LCM showed an increase in cell number and growth of the cells was observed for approximately 3 days and then decreased.

Objective/Scope Accurate well production rate measurement is critical for reservoir management. The production rate measurement is carried out using surface devices, such as orifice flow meter and venturi flow meter. For large offshore fields development with a high number of wells, the installation and maintenance costs of these flowmeters can be significant. Therefore, an alternative solution needs to be developed. This paper described the successful implementation of Artificial Intelligence in predicting the production rate of big-bore gas wells in an offshore field. Methods, Procedures, Process Successful application of AI depends on capitalizing on a large set of data. Therefore, flowing parameters data were collected for more than 30 gas wells and totaling over 100,000 data points. These wells are producing gas with slight solid production from a high-pressure high-temperature field. In addition, these wells are equipped with a multistage choke that reduces the noise and vibration levels. An Artificial Neural Network is trained on the data using Gradient Descent method as the optimization algorithm. The network takes as an input the upstream and downstream pressure and temperature, and the choke size. The output is the gas rate measured in MMscf/day. Results, Observations, Conclusions The data set was divided into 70% for training the neural network and 30% for validation. Artificial Neural Network (ANN) was used and the developed model compared exceptionally well with the gas rates measured from the calibrated venturi meters. The gas rate estimation was within a 5% error. The model was developed for two types of completions: 7" and 9-5/8" production tubing. One of the challenges was how to estimate the choke wear which plays a major role in the quality of the choke size data. A linear choke wear deterioration is applied in this case, while work in progress is taking place for acquiring acoustic data that can significantly improve the choke wear modeling. Novel/Additive Information The novel approach presented in this paper capitalizes on Al analytics for estimating accurate gas flow rate values. This approach has improved the reservoir data management by providing accurate production rate values which has drastically improved the reservoir simulation. Moreover, the robustness of the AI model has forced us to rethink the conventional design of installing a flow meter for every well. As shown in this paper, the AI model served as an alternative to conventional venturi meters. We believe that the application of AI models to other aspects of production surveillance will lead to a shift into how operators design production facilities.