Analyzing the Statistics Function for Determination of Oil Flow Rate Equation in New Productive Zone

Oil rate will be decline at production time in a well. So, we have to produce in another layer who assume have a potential. Before we produce another layer who assumed have a potential, we need to predict oil rate to known how much oil gain. In this field research oil rate prediction in new productive zone was determine following by analogical data and near well references. In this method there is a difference determine of oil rate for each people. Cause of that, in this research using analysis statistical for oil rate predicting in new productive zone based on linear function for Productivity Index (PI) and polynomial function for watercut. Determining equation of linear and polynomial functions for oil rate prediction measuring by production and logging data for each well who assumed productive zone in area X field RMT. Based of statistically analysis for linear function known that coefficient determination (r2) = 0.9964 and polynomial function known that coefficient determination (r2) = 0.9993. This result indicated that we can use both of the functions for oil rate prediction in new productive zone in area X field RMT. After that, based on both of functions calculate oil rate prediction each wells in area X field RMT. So, known differences in oil rate prediction between oil rate data in area X field Y known is 28.13 BOPD or 0.78%.


Int r oduct i on
The decline of oil flow rate in an oil field becom es a problem that have to be faced during the production period. One of several w ays to solve the declining oil flow rate problem is by producing a new zone. Previously, oil flow rate determ ination in the new zone t hat have not been producing at a potential reservoir is determined from the logging data and w ells near by reference (Gollan, M ichael. W hitson, Curtis H,1996). This m ethod focuses on the analogy of the existing data. By using t hese m ethods, several param eters that becom e the benchm ark of oil flow rate estim ation have an uncertainty factor. In this case, everyone has the different determ ination of an oil flow rate w ith the sam e param eters. It m akes this research needs to be done to determ ine that uncertainty factor. Potential reservoir w hich is the becom es the object in this research shall be referred to the productive zone (Kelkar, 2002).
Productive zone in this study is the layer that has never been in produces by a w ell, so it becom es a backup for the w ell. This occurs because the w ell w as still quite good producing from another layer or from w ells t hat are still relatively new , so there arecertain zone that has never been produced. W hen production w ells dow n then, can be done to increase production by opening new layers that are considered productive. (Ariadji, Tutuka. Radjes, 2012) In t he case of m anagem ent and these issues , it is often found som e forecasting activity, prediction, estim ation and m ore. One method that can be used to solve the problem is statistical m ethods. The used of statistical m ethod sare very dependent on the structure of the data or the number of variables (Stroud K.A and J. Dexter, 2003) . One of the m ethod that is used for one variable or m ore than one variable is the regression analysis (Stroud K.A and J. Dexter, 2003) .
Regression analysis is a statistical m ethodology to predict the value of one or m ore response variables (variable dependen) from the collection of predictor variable value (variable independen) . This analysis can also be used to predict or forecast the effect of the predictor variable (independent variable) on the response. In regression analysis , it is learn how does t hese variables relat e and expressed in a m athem atical function.This research is done by using regression analysis, to determ ine the function representing t he approxim ate flow rate of oil in the productive zone (Jothikumat, 2004).
The objective of this paper is to determ ine the coefficients and function of linear regression of the perm eability and thickness of the perforation of the Productivity Index and regression function at the polynom ial correlat ion to the w ater saturation of the W atercut. At the end w e could to estim ate the flow rate of the oil in the productive zone using a regression function and evaluation of oil flow rate estim ates based on the function of the oil flow rate based on the data.

M at er i al and M et hods
Productive zone in this study is a new zone that has not been produced and has potential if seen from the data logging. This study uses data of each w ell log consisting of log GR (Gamm a Ray), log SP (Spontaneous Potential), caliper logs, resistivity logs, neutron and density logs. Based on the GR deflection curve at m inim um value, indicates that the area w ith the curve approaching the minim um value m ay be a reservoir layers because of thenonshale (perm eable) rock type w hichin this case, the sandstone type, the reservoir rock type in general. M ean w hile, if the deflection curve leads to a m axim um value then t he rock type may be shale (im perm eable).
On the log resistivity deflectioncurve w ith a great value indicates the potential for hydrocarbons contained therein, on the contrary if the deflection curve w ith a sm all resistivity values indicates the potential non-hidrokarbon (w ater zone). From the result s of neutron log that has a deflection at a great value, it can be seen t hat these rocks have a large porosity. In t he productive reservoir layers, the neutron-density log curves w ill intersect and form of separation. This indicates the exist of perm eable layer and a reservoir layer. This both curvesshow s t he form ation of separation column (cross over). The sm all cross over indicates the type of fluid is oil. At the gas zone, these tw o curves show t he form ation of the separation column. A large cross over, gas zone is also characterized by neutron porosity price that is far less than the price of porosity, so it w ould show the existence of a larger separation.
In this research, to determ ine the flow rate of oil in the productive zone, it w ould require som e data from w ells located in an area that is not separated by any fault (fault). A layer of sand that is used as data in this study is the sam e sand layer. This is done because t he consideration of the physical properties of rock and fluid at the sam e sand tends not m uch different w hen com pared to the physical properties of fluids and rocks on different sand.
In areas 1 and 3 t here are 614 w ells candidates w hich are productive zones that have been produced. How ever, this research is lim ited to areas that are not separated by their fault, so the area that it is included into non-separated by fault area is area 1w ith focus area 1, 2, 3 and area 3 w ith focus area 5 there are only 104 w ells. After determ ining the candidate w ells that are included in the areas relevant to the objectives of this study, furtherm ore, pick the sam e sand layer seen in a predeterm ined area. In this study, A-1 sand layer chosed.
Of the 104 w ells w hich are review ed there w ere 21 w ells t hat have a productive zone A-1. Furtherm ore in this study, t he 21 w ell candidates is review ed as productive zones to estim ate the oil flow rate. Perm eability and saturation datain the productive zone w hich is used as a candidate in this research w as determ ined from logging data to the log attached. W hile the thickness of the zone productive in this study is t he interval thickness of each w ell perforations know n by looking at the production history of candidate w ells w hich is about to be examined and retrieve perforation data (Top perforation and bottom perforation), the w atercut data and production flow rate on the candidate w ells in this research.
From the LINEST functions output above, do the t value and F value calculation to determ ine w het her t he function of the resulting statistics can be accepted. Calculation of PI' based on Linear Functions to Absolut Delt a PI perform ed to determ ine the percentage of PI errors and differences of each w ell, so t he result s got in Table  3.  From the field data can be conducted to determ ine the regression coefficients, to obtain the correlation polynom ial to predict W C w ith LINEST function as show n in table 4.  From the LINEST function output in Table 4 generated the polynom ial function to estim ate W C is: WC ′ = 553.45Sw − 735.14Sw 2 + 332.02Sw 3 − 48.28 From the LINEST function output above, calculate the t value and F value to determ ine w het her t he function of the resulting acceptable statistically. Calculation w as perform ed on each w ell to get the oil flow rate w ith a linear function of kh p regression of the Productivity Index and polynom ial functions for Sw regression against w atercut generated at the output function LINEST, so it can be tabulated as show n in Table 5. Plot betw eenQo and Qo 'to each w ell, can be seen in Fig 5.   Based on the calculations perform ed to estim ate the oil flow rate based on function, then from the tw enty-one (21) w ells studied,it is know n the total of oil flow rate is 3633.68 BOPD. W hile from the data is know n that oil flow rate total of tw ent y-one w ell studied is 3605.55 BOPD. From these result s, note t he difference oil flow rate based on the data of the oil flow rate based function is 28.13 BOPD. The percentage error of both oil flow rate is 0.78%.
After assessing the w atercut from w ater saturation data and Productivity index from perm eability data, the thickness of the perforation of each w ell, then perform ed the calculations of oil flow rate using both equation for estim ating the flow rate of oil in new productive zones.

Conclussi on
Based on the research are: 1. Estim ated oil flow rate can be m ultiplied by the thickness of the perforation perm eability param eters (k.h p ) to determ ine the productivity index w ith r 2 = 0.9964. W hile w ater saturation param eters can be used to determ ine w atercut of polynom ial functions w ith r2 = 0.9993 2. The regression coefficient for k.h p know n by using LINEST function in Excel is 2.92x10-3, intercept is 1,49 w hile t he Sw regression coefficient is 397.83, Sw 2 is (-5402.47), Sw 3 is 140.53 intercept is (-35