Mohamed, Abdelmjeed (King Fahd University of Petroleum & Minerals) | Ahmed, Musa (King Fahd University of Petroleum & Minerals) | Mohamed, Ismail (Innoenergy Master School) | Abdein, Moutaz (University of Khartoum)
The knowledge of PVT properties is of a high importance in the petroleum industry, precisely, in oil and gas reservoirs development, engineering, and production. In the practice of petroleum industry, these properties are obtained by laboratory analysis from downhole fluid samples which are highly recommended to be taken in the early time of the reservoir prior to production to get representative reservoir fluid. It becomes a challenge when production is started as the fluid composition in the reservoir changes with changing the reservoir pressure, in such case PVT properties could be estimated using measured data on the surface separator, well head, offset field, etc. However, in many cases this data is not available and the only source to estimate these properties is the empirically derived correlations. In fact, there are many correlations that have been developed in the past seven decades for different geological areas. Nevertheless, these correlations are merely reliable for a specific range of data. Therefore, significant errors might yield when these correlations are applied for different regions and crude oils.
This work is the first regional and formal analysis that studies the performance of the most popular PVT correlations and introduces a new set of correlations that estimate some of the PVT properties of different Sudanese crude oils which are: bubble point pressure (Pb), solution gas-oil ratio (Rs), and oil formation volume factor (Bo), at pressures below the bubble point pressure.
The data was obtained from different Sudanese oilfields, to represent a wide range of data. Then, the data was analyzed, prepared and fitted with correlations using non-linear regression techniques. Afterwards, statistical analyses were performed, and the new correlations were compared with the existing correlations to prove their applicability using the most common statistical parameters, i.e. average absolute percentage error, standard deviation and R-square.
The Statistical Error Analyses (SEA) show that all the popular existing correlations yield a variable range of accuracy when these correlations are applied to our database and the proposed correlations were found to be superior to all the popular existing correlations in terms of accuracy when applied for this specific region. Eventually, the performance of the newly developed correlations was tested against each oilfield individually to ensure their accuracy, and it was found that the error varies widely from field to another.
It is concluded from this study that PVT properties vary regionally and recommendation comes to perform such study before using such correlations in estimating PVT properties for a specific region.
The horizontal wells are applied to enhance the production rate by increasing the contact area between the wellbore and reservoir, it has been also used to access the highly heterogeneous and unconventional formations. One horizontal well can produce the same amount of 5 vertical wells with a very competitive cost and operational time. Further improvement for the productivity of horizontal well can be achieved by conducting hydraulic fracture operations, especially for low permeable or unconventional formations. This paper shows a new technique to estimate the performance of hydraulically fractured horizontal wells, without a need for using downhole valves or smart completion.
In the literature, few empirical models have been proposed to evaluate the inflow performance of such wells. However, most of these models assume constant pressure drop in the horizontal section, therefore, significant errors were reported from those models. In this work, a reliable model will be presented to predict the well deliverability for hydraulically fractured horizontal well producing from heterogeneous and anisotropic formation. Different artificial intelligence (AI) methods were investigated to evaluate the well performance using a wide range of reservoir/wellbore conditions. The significant of several parameters on the well productivity were investigated including; permeability ratio (kh/kv), number of fracture stages and the length of horizontal section.
The AI model was developed and validated using more than 300 data sets. Artificial neural network (ANN) model is built to determine the production rate with an acceptable error of 8.4%. The model requires the wellbore configurations and reservoir parameters to quantify the flow rate. No numerical approaches or downhole well completions were involved in this ANN model, which reduce the running time by avoiding such complexity. Moreover, a mathematical relation was extracted from the optimized artificial neural network model. In conclusion, this work would afford an effective tool to determine the performance of complex wells, and reduce the differences between the actual production data and the outputs of commercial well performance software.