"Marginal field" was introduced to the oil and gas industry to identify those fields that have negative economic effects in its development. More specifically it is possible to define a marginal field as a field that is cost ineffective to develop with conventional oil and gas means of technology. Economic development of marginal fields in most cases requires the use of existing processes to minimize cost of finding evolving technologies in development of reserves. This paper generally evaluates the feasibility of using the enhanced oil recovery technique to improve reserves in a marginal field operation environment. A marginal heavy oil field in the offshore environment of the Niger Delta region which started production in 2011 is used as a case study to evaluate the feasibility of the use of enhanced oil recovery method to improve recovery. Due to poor mobility ratio in this heavy oil field and its associated big aquifer sizes, pockets of unrecovered oil have been left behind the water fronts and water cut has risen above 80% in most of the producing wells. Recent integrated field evaluation shows that the recovery factor is poor compared to the size of oil originally in place and this triggered the need to process subsurface assessments of developing such reserves that exist in any marginal field using enhanced oil recovery technique. This paper therefore goes through the fundamental scope of an enhanced oil recovery study process to determine the applicability of this technology in a marginal oil field.
With the growing importance and application of Machine Learning in various complex operations in the Oil and Gas Industry, this study focuses on the implementation of data analytics for estimating and/or validating bottom-hole pressure (BHP) of Electrical Submersible Pump (ESP) wells. Depending on the placement of the ESP in the wellbore and fluid gravity of the well fluid, there can be little or no difference between BHP and Pump intake Pressure (PIP); hence these two parameters were used interchangeably. The study focuses majorly on validating PIP when there are concerns with downhole gauge readings. It also has application in estimating PIP when the gauge readings are not available, provided the relevant ESP parameters are obtainable. ESP wells generally have gauges that operate on "Comms-on-Power" principle i.e. downhole communication is via the power cable and loss of signal occurs when there is no good electrical integrity along the electrical path of the ESP system. For proper hydrocarbon accounting and statutory requirements, it is important to have downhole pressure readings on a continuous basis, however this cannot be guaranteed throughout the life cycle of the well. Therefore, an alternative method is essential and had to be sought. In this study, the Response Surface Modelling (RSM) was first used to generate a model relating the ESP parameters acquired real-time to the PIP values. The model was fine-tuned with a Supervised Machine Learning algorithm: Artificial Neural Network (ANN). The performance of the algorithms was then validated using the R-Square and Mean Square Error values. The result proves that Machine Learning can be used to estimate PIP in a well without recourse to incurring additional cost of deploying new downhole gauges for acquisition of well and reservoir data.
The field under consideration is located about 50 Km offshore Nigeria in water depth of about 130ft. The field was discovered in 1968 with X1 well. This particular well encountered oil in four different zones (IX, X, XI and XII) in the Pliocene Agbada formation. The X1 well was logged and tested to evaluate the potential. The second well, X2 was drilled in the early 70s and that was in a separate fault block. This second well encountered oil in two reservoirs, IX and X. The XI was poorly developed in X2 well relative to X1 well which was found wet. The XII reservoir was encountered with good oil shows and gas readings were found in poorer sections of the reservoir. A third well was drilled which did not encounter any hydrocarbon and many of the sands were poorly developed or absent relative to the X1 and X2 wells. No testing was conducted in this well. The field was appraised with X4 well. The X4 well encountered oil in all the four sands. Pressure and log data were taken from the reservoirs in this well and the well was tested to know the true potential of each. The reservoir sands are of good to excellent quality but are unconsolidated and sand control will be required in the development phase. The fluid quality is 25 deg API with moderate viscosity and a moderate GOR in the XII, and about 14 deg API with high viscosity and low GOR in the other reservoirs. Reservoir pressure and temperature is normal in the IX sand, slightly over-pressured in the X sand and significantly over-pressured in the XII. Following the successful results of the subsequent appraisal programme, the reserves level increased significantly to more than 40 MMbbls. At this level the field was judged large enough to support a stand alone development. This had allowed a first proposal to initiate an initial development plan for the Field. Development drilling commenced in 2009 with first oil was recorded in 2010. As the development of the field progressed the lessons learnt from first development phase of drilling were implemented in the second phase. This led to better wells with improved production rates. In addition, effective reservoir management in the field has led to optimized production which saw recovery factor in these oil rim reservoirs getting above 30%. This paper highlights the challenges encountered, innovative solutions and key learnings along each phase of development.
Heavy oil makes up approximately 15 percent of the world's remaining oil reserves. It presents opportunities that could be commercially viable but often rejected because of the inherent challenges of producing them, especially in offshore environments. These challenges include flow assurance, produced water separation and treatment and additional heat and power requirements. The common occurrence of low API reservoirs at relatively shallow depths can lower drilling and completions costs but can also increase production and transport costs and limit marketing options. This paper describes the commercially successful production of a heavy oil reservoir offshore Niger-Delta. The field was discovered in 1968 and an appraisal well was drilled in 1970. At that time, the accumulations were deemed to be too small to develop. Forty years later the economics of oil production had changed and additional appraisal wells were drilled. Modern logs, core and MDT samples were taken. Three of the five reservoirs contained heavy oil. Reservoir simulations established initial estimates of recovery for each reservoir encountered and IPM models established inflow-outflow relationships. For the heavy oil reservoirs, the IPM showed that artificial lift would be required to produce the oil. This was confirmed by failed attempts to naturally flow an appraisal well that tested the best of the heavy oil reservoirs. The decision was taken to drill a horizontal pilot producer completed with a downhole ESP. The pilot producer was successful and several development wells were subsequently drilled in that reservoir and completed with ESP and gas lift capability. This paper further showcases how the economics of producing a heavy oil reservoir was reversed by comingling with lighter crude and managing the challenges of crude compatibilities, produced water separation and marketing of the crude blend.
Drilling extended open hole sections which carries significant operational risks especially in pressured reservoirs has most times resulted to wellbore instability problems which could cause stuck pipe problems. However, drilling designs or operational challenges may lead to such risky decisions. When such a situation arise, precautionary strategies are normally put in place to ensure that potential wellbore instability does not cause loss of time or resources. The paper narrates the case of a well drilled in the Niger delta environment by one of the marginal field operators. The tophole was planned to be drilled with one hole section, but due to difficulties encountered while drilling to the planned depth, decision was made to sidetrack the well with a smaller 6-1/2"hole section. The well was finally landed successfully. The objective of this paper is to narrate the lessons learned in drilling an open hole interval, the challenges and the consequent decisions taken to land the well in the hydrocarbon interval. It discusses both the well design and actual subsurface challenges, with significant emphasis in the strategy adopted in having landing into the objective sand. Some of the key learnings are the need to get much information as possible when drilling into a compartmentalized reservoir. A thorough knowledge of a reservoir helps in adequate optimization during drilling and geosteering.
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